Natural language processing techniques for document summarization using local and corpus-wide inferences

ABSTRACT

As described herein, various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing natural language processing operations using a combination of a cross-token attention machine learning, a cross-utterance attention machine learning model, and an integer linear programming joint keyword-utterance optimization model to select an extractive keyword summarization of a multi-party communication transcript data object that comprises a selected utterance subset of U utterances (e.g., U sentences) of a document data object and a selected keyword subset of K candidate keywords of the document data object.

BACKGROUND

Various embodiments of the present invention address technicalchallenges related to performing natural language processing and providesolutions to address the efficiency and reliability shortcomings ofexisting natural language processing solutions.

BRIEF SUMMARY

In general, various embodiments of the present invention providemethods, apparatus, systems, computing devices, computing entities,and/or the like for performing natural language processing operationsusing a combination of a cross-token attention machine learning, across-utterance attention machine learning model, and an integer linearprogramming joint keyword-utterance optimization model to select anextractive keyword summarization of a multi-party communicationtranscript data object that comprises a selected utterance subset of Uutterances (e.g., U sentences) of a document data object and a selectedkeyword subset of K candidate keywords of the document data object.

In accordance with one aspect, a method is provided. In one embodiment,the method comprises: (i) identifying a plurality of utterancesassociated with a document data object; (ii) for each utterance: (a)generating, using a cross-utterance attention machine learning model, anattention-based utterance representation, wherein the cross-utteranceattention machine learning model is configured to: (1) for eachutterance pair, generate a cross-utterance self-attention weight, and(2) generate the attention-based utterance representation for theutterance based at least in part on each cross-utterance self-attentionweight that is associated with the utterance, (b) generating, based atleast in part on the attention-based utterance representation and anutterance-based document representation that is generated based at leastin part on each attention-based utterance representation, adocument-utterance similarity score for the utterance, and (c)generating, based at least in part on a local utterance correlationgraph data object and the document-utterance similarity score for theutterance, an utterance score for the utterance, wherein each utterancecorrelation edge of the local utterance correlation graph data objectcorresponds to a respective utterance pair and is associated with anutterance correlation edge weight that is generated based at least inpart on the cross-utterance self-attention weight for the respectiveutterance pair; (ii) generating an extractive summarization based atleast in part on each utterance score; and (iii) performing one or moreprediction-based actions based at least in part on each utterance score.

In accordance with another aspect, an apparatus comprising at least oneprocessor and at least one memory including computer program code isprovided. In one embodiment, the at least one memory and the computerprogram code may be configured to, with the processor, cause theapparatus to: (i) identify a plurality of utterances associated with adocument data object; (ii) for each utterance: (a) generate, using across-utterance attention machine learning model, an attention-basedutterance representation, wherein the cross-utterance attention machinelearning model is configured to: (1) for each utterance pair, generate across-utterance self-attention weight, and (2) generate theattention-based utterance representation for the utterance based atleast in part on each cross-utterance self-attention weight that isassociated with the utterance, (b) generate, based at least in part onthe attention-based utterance representation and an utterance-baseddocument representation that is generated based at least in part on eachattention-based utterance representation, a document-utterancesimilarity score for the utterance, and (c) generate, based at least inpart on a local utterance correlation graph data object and thedocument-utterance similarity score for the utterance, an utterancescore for the utterance, wherein each utterance correlation edge of thelocal utterance correlation graph data object corresponds to arespective utterance pair and is associated with an utterancecorrelation edge weight that is generated based at least in part on thecross-utterance self-attention weight for the respective utterance pair;(ii) generate an extractive summarization based at least in part on eachutterance score; and (iii) perform one or more prediction-based actionsbased at least in part on each utterance score.

In accordance with yet another aspect, a computer program product isprovided. The computer program product may comprise at least onecomputer-readable storage medium having computer-readable program codeportions stored therein, the computer-readable program code portionscomprising executable portions configured to: (i) identify a pluralityof utterances associated with a document data object; (ii) for eachutterance: (a) generate, using a cross-utterance attention machinelearning model, an attention-based utterance representation, wherein thecross-utterance attention machine learning model is configured to: (1)for each utterance pair, generate a cross-utterance self-attentionweight, and (2) generate the attention-based utterance representationfor the utterance based at least in part on each cross-utteranceself-attention weight that is associated with the utterance, (b)generate, based at least in part on the attention-based utterancerepresentation and an utterance-based document representation that isgenerated based at least in part on each attention-based utterancerepresentation, a document-utterance similarity score for the utterance,and (c) generate, based at least in part on a local utterancecorrelation graph data object and the document-utterance similarityscore for the utterance, an utterance score for the utterance, whereineach utterance correlation edge of the local utterance correlation graphdata object corresponds to a respective utterance pair and is associatedwith an utterance correlation edge weight that is generated based atleast in part on the cross-utterance self-attention weight for therespective utterance pair; (ii) generate an extractive summarizationbased at least in part on each utterance score; and (iii) perform one ormore prediction-based actions based at least in part on each utterancescore.

BRIEF DESCRIPTION OF THE DRAWINGS

Having thus described the invention in general terms, reference will nowbe made to the accompanying drawings, which are not necessarily drawn toscale, and wherein:

FIG. 1 provides an exemplary overview of an architecture that can beused to practice embodiments of the present invention.

FIG. 2 provides an example predictive data analysis computing entity inaccordance with some embodiments discussed herein.

FIG. 3 provides an example client computing entity in accordance withsome embodiments discussed herein.

FIG. 4 is a flowchart diagram of an example process for generating anextractive summarization for a multi-party communication transcript dataobject in accordance with some embodiments discussed herein.

FIG. 5 provides an operational example of a multi-party communicationtranscript data object in accordance with some embodiments discussedherein.

FIG. 6 is a data flow diagram of an example process for generatingkeyword scores for extracted/inferred candidate keywords of a of amulti-party communication transcript data object in accordance with someembodiments discussed herein.

FIG. 7 is a flowchart diagram of an example process for generatingutterances scores for utterances of a multi-party communicationtranscript data object in accordance with some embodiments discussedherein.

FIG. 8 provides an operational example of a cross-utterance attentionmachine learning model in accordance with some embodiments discussedherein.

FIG. 9 provides an operational example of a prediction output userinterface in accordance with some embodiments discussed herein.

DETAILED DESCRIPTION

Various embodiments of the present invention now will be described morefully hereinafter with reference to the accompanying drawings, in whichsome, but not all, embodiments of the inventions are shown. Indeed,these inventions may be embodied in many different forms and should notbe construed as limited to the embodiments set forth herein; rather,these embodiments are provided so that this disclosure will satisfyapplicable legal requirements. The term “or” is used herein in both thealternative and conjunctive sense, unless otherwise indicated. The terms“illustrative” and “exemplary” are used to be examples with noindication of quality level. Like numbers refer to like elementsthroughout. Moreover, while certain embodiments of the present inventionare described with reference to predictive data analysis, one ofordinary skill in the art will recognize that the disclosed concepts canbe used to perform other types of data analysis.

I. OVERVIEW AND TECHNICAL IMPROVEMENTS

Various embodiments of the present invention disclose techniques forimproving storage efficiency of document storage systems. As describedherein, various embodiments of the present invention disclose techniquesfor generating extractive summarizations of document data objects thatcomprise a selected utterance subset of the utterances of each documentdata object and a selected keyword subset of the candidate keywords ofeach document data object. Because an extractive summarization of adocument data object is smaller in size than the underlying documentdata object (as the extractive summarization includes subsets ofcandidate keywords and utterances described by the document dataobject), various embodiments of the present invention enable storingextractive summarizations of document data objects instead of thedocument data objects that are bigger in size. In this way, variousembodiments of the present invention reduce storage requirementsassociated with storing document data, and thus increase storageefficiency of storing document data associated with document dataobjects. Accordingly, by generating extractive summarizations ofdocument data objects that comprise a selected utterance subset of theutterances of each document data object and a selected keyword subset ofthe candidate keywords of each document data object, various embodimentsof the present invention disclose techniques for improving storageefficiency of various document storage systems.

Furthermore, various embodiments of the present invention make importanttechnical contributions to improving predictive accuracy of naturallanguage processing machine learning models that are configured toperform natural language processing operations on document data objectsby using an integer linear programming joint keyword-utteranceoptimization model that generates a selected utterance subset of theutterances of a document data object and a selected keyword subset ofthe candidate keywords of the document data object in a manner that isconfigured to maximize a joint keyword-utterance score for the selectedutterance subset and the selected keyword subset given one or moreinteger linear programming optimization constraints, an approach whichin turn improves training speed and training efficiency of training thenoted natural language processing machine learning models. It iswell-understood in the relevant art that there is typically a tradeoffbetween predictive accuracy and training speed, such that it is trivialto improve training speed by reducing predictive accuracy, and thus thereal challenge is to improve training speed without sacrificingpredictive accuracy through innovative model architectures, see, e.g.,Sun et al., Feature-Frequency—Adaptive On-line Training for Fast andAccurate Natural Language Processing in 40(3) Computational Linguistic563 at Abst. (“Typically, we need to make a tradeoff between speed andaccuracy. It is trivial to improve the training speed via sacrificingaccuracy or to improve the accuracy via sacrificing speed. Nevertheless,it is nontrivial to improve the training speed and the accuracy at thesame time”). Accordingly, techniques that improve predictive accuracywithout harming training speed, such as the techniques described herein,enable improving training speed given a constant predictive accuracy. Indoing so, the techniques described herein improving efficiency and speedof training natural language processing machine learning models, thusreducing the number of computational operations needed and/or the amountof training data entries needed to train natural language processingmachine learning models. Accordingly, the techniques described hereinimprove at least one of the computational efficiency, storage-wiseefficiency, and speed of training natural language processing machinelearning models.

Moreover, various embodiments of the present invention make importanttechnical contributions to improving resource-usage efficiency ofpost-prediction systems by using extractive summarizations to set thenumber of allowed computing entities used by the noted post-predictionsystems. For example, in some embodiments, a predictive data analysiscomputing entity determines D document classifications for D documentdata objects based at least in part on the D extractive summarizationsfor the D document data objects. Then, the count of document dataobjects that are associated with an affirmative document classification,along with a resource utilization ratio for each document data object,can be used to predict a predicted number of computing entities neededto perform post-prediction processing operations (e.g., automatedinvestigation operations) with respect to the D document data objects.For example, in some embodiments, the number of computing entitiesneeded to perform post-prediction processing operations (e.g., automatedinvestigation operations) with respect to D document data objects can bedetermined based at least in part on the output of the equation:

${R = {{ceil}\left( {\sum\limits_{k}^{k = K}{ur}_{k}} \right)}},$where R is the predicted number of computing entities needed to performpost-prediction processing operations with respect to the D documentdata object, cello) is a ceiling function that returns the closestinteger that is greater than or equal to the value provided as the inputparameter of the ceiling function, k is an index variable that iteratesover K document data objects among the D document data that areassociated with affirmative investigative classifications, and ur_(k) isthe estimated resource utilization ratio for a kth document data objectthat may be determined based at least in part on a count ofutterances/tokens/words in the kth document data object. In someembodiments, once R is generated, the predictive data analysis computingentity can use R to perform operational load balancing for a serversystem that is configured to perform post-prediction processingoperations (e.g., automated investigation operations) with respect to Ddocument data objects. This may be done by allocating computing entitiesto the post-prediction processing operations if the number of currentlyallocated computing entities is below R, and deallocating currentlyallocated computing entities if the number of currently allocatedcomputing entities is above R.

Various embodiments of the present invention relate to generating a callsummary by extracting relevant key phrases and relevant key utterancesfrom a transcript document using integer linear programming optimizationoperations, wherein: (i) the integer linear programming optimizationoperations are performed based at least in part on utterance rankingscores for utterances of the transcript document and key phrase rankingscores for key phrases of the transcript document, (ii) the utteranceranking scores are generated via processing an utterance relationshipgraph that is generated using cross-attention weights for utterancesusing a text graph algorithm that incorporates utterance-documentsimilarity measures as priors, and (iii) the key phrase ranking scoresare generated via processing a key phrase relationship graph that isgenerated using cross-attention weights for key phrases using a textgraph algorithm that incorporates phrase-document similarity measures aspriors.

II. DEFINITIONS

The term “candidate keyword” may refer to a data construct thatdescribes a collection of one or more text tokens (e.g., words) of amulti-party communication transcript data object (or other document dataobject), where the candidate keywords may be selected to be included aspart of a keyword summary section of an extractive summarization for themulti-party communication transcript data object. Accordingly, eachcandidate keyword of a multi-party communication transcript data objectmay be associated with a keyword-related token subset of the text tokensof the multi-party communication transcript data object. For example, if“protected health information” is a candidate keyword of the multi-partycommunication transcript data object, then the keyword-related tokensubset for the particular candidate keyword may be the set of texttokens {“protected”, “health”, “information”}. In some embodiments, thecandidate keywords of a multi-party communication transcript data objectare generated by combining text tokens of the multi-party communicationtranscript data object into units based at least in part on keywordgeneration heuristics. For example, in some embodiments, a keywordgeneration heuristic may require that, each time a text token that isclassified as being a noun by a part-of-speech tagger model follows atext token that is classified as being an adjective by thepart-of-speech tagger model, then the combination of the two text tokensis grouped into a candidate keyword. As another example, a keywordgeneration heuristic may require that each S-sized sequence ofconsecutively occurring text tokens of a multi-party communicationtranscript data object is grouped into a respective candidate keyword,where S may be selected a range of one or more size values defined byconfiguration data for a corresponding summarization framework. Forexample, if S is selected from the size range {3, 4, 5}, then the set ofcandidate keywords for a multi-party communication transcript dataobject includes all of the three-sized sequences ofconsecutively-occurring text tokens of the multi-party communicationtranscript data object (e.g., the sequence “significant medicalmilestone), all of the four-sized sequences of consecutively-occurringtext tokens of the multi-party communication transcript data object(e.g., the sequence “very significant medical milestone”), and all ofthe five-sized sequences of consecutively-occurring text tokens of themulti-party communication transcript data object (e.g., the sequence“early signs of acute prostate cancer” assuming stop words such as “of”are removed prior to candidate keyword generation). As this exampleillustrates, depending on the set of keyword generation heuristics used,in some embodiments a text token may belong to two or more candidatekeywords.

The term “cross-token attention machine learning model” may refer to adata construct that describes parameters, hyperparameters, and/ordefined operations of a machine learning model that is configured togenerate, for each text token of N text tokens of a multi-partycommunication transcript data object, an attention-based tokenrepresentation. In some embodiments, the cross-token attention machinelearning model is further configured to: (i) generate a set of N*N tokenpairs each comprising a pair of text tokens of the N text tokens of themulti-party communication transcript data object, (ii) for each tokenpair, generate a cross-token self-attention weight (e.g., using aself-attention mechanism), and (ii) generate the attention-based tokenrepresentation for the text token based at least in part on (e.g., byapplying a non-linear function to a linear combination of) eachcross-token self-attention weight that is associated with the texttoken. In some embodiments, during training, the cross-token attentionmachine learning model is trained using a language modeling task, suchas a token making task that comprises: (i) masking a randomly-selectedM-sized subset of the N text tokens of a training document data object(e.g., a training multi-party communication transcript data object);(ii) processing initial token representations (e.g., bag of wordsrepresentations) of the (T−M) non-masked tokens of the training documentdata object and M masked token representations (e.g., all-zero vectorrepresentations) of the masked tokens of the training document dataobject in accordance with a current state of trainable parameters of thecross-token attention machine learning model to: (a) for each token pairof N*N token pairs that comprises two text tokens (including token pairsthat comprise masked text tokens), generate a cross-token self-attentiontoken, (b) for each text token that is in N token pairs and thus isassociated with N cross-token self-attention weights, generate anattention-based token representation based at least in part on a linearcombination of a vector of the N cross-token self-attention weights forthe text token and a vector of the N initial token representations forall of the N text tokens, and (c) predict the M masked tokens of thetraining document data object based at least in part on the Nattention-based token representations for the N text tokens of thetraining document data object; and updating the trainable parameters ofthe cross-token attention machine learning model to minimize an errormeasure describing an estimated/computed error between the predicted Mmasked tokens and the actual/ground-truth M masked tokens. In thisexample, it is to be understood that while N may be defined by the sizeof the training document data object, Mmay be defined by ahyperparameter of the cross-token attention machine learning model.

The term “attention-based token representation” may refer to a dataconstruct that describes a computed/predicted fixed-size representationof a corresponding text token that may be generated based at least inpart on cross-token self-attention weights that describes semanticcorrelation of the corresponding token with other tokens of acorresponding document data object. In some embodiments, once trainedand during runtime, the cross-token attention machine learning modeluses the resulting trained parameters to process initial tokenrepresentations (e.g., bag of words representations) of text tokens ofan input document data object to generate, for each text token, arespective attention-based token representation. For example, in someembodiments, during runtime, the cross-token machine learning model: (i)receives N input vectors each comprising an initial token representationof N initial token representations for a respective text token of N texttokens of the input document data object, (ii) for each token pair of Ntoken pairs that comprises two text tokens, generates, using the trainedparameters resulting from the training operations, an attention-basedtoken representation, and (iii) for each text token that is in N tokenpairs and thus is associated with N cross-token self-attention weights,generates an attention-based token representation based at least in parton a linear combination of a vector of the N cross-token self-attentionweights for the text token and a vector of the N initial tokenrepresentations for all of the N text tokens. Accordingly, the input tothe cross-token attention machine learning model may include a set of Ninput token representation vectors for the N input tokens of an inputdocument data object, while the output of the cross-token attentionmachine learning model may include a set of N attention-based tokenrepresentations for the N input tokens of an input document data object.In this example, it is to be understood that N may be defined by thesize of the training document data object.

The term “cross-token self-attention weight” may refer to a dataconstruct that describes a computed/predicted measure of semanticrelevance of a first text token of a document data object for a secondtext token of the document data object. In some embodiments, thecross-token self-attention weights for token pairs are determined basedat least in part on the trained parameters of a cross-token attentionmachine learning model. Accordingly, the cross-token self-attentionweights may be dynamically generated values. For example, in someembodiments, during runtime, the cross-token machine learning model: (i)receives N input vectors each comprising an initial token representationof N initial token representations for a respective text token of N texttokens of the input document data object, (ii) for each token pair of Ntoken pairs that comprises two text tokens, generates, using the trainedparameters resulting from the training operations, an attention-basedtoken representation, and (iii) for each text token that is in N tokenpairs and thus is associated with N cross-token self-attention weights,generates an attention-based token representation based at least in parton a linear combination of a vector of the N cross-token self-attentionweights for the text token and a vector of the N initial tokenrepresentations for all of the N text tokens.

The term “token-based keyword representation” may refer to a dataconstruct that describes a fixed-size representation of a correspondingcandidate keyword that is generated based at least in part on theattention-based keyword representations that are in the candidatekeyword (i.e., that are in the keyword-related token subset for thecandidate keyword). In some embodiments, when an ith candidate keywordcomprises N_(i) text tokens and thus N_(i) attention-based keywordrepresentations, the token-based keyword representation is generatedbased at least in part on (e.g., by averaging, by concatenating, byprocessing using one or more trained neural network layers, and/or thelike) the N_(i) attention-based keyword representations. Accordingly, insome embodiments, a token-based keyword representation provides acomputed/predicted representation of a corresponding candidate keywordthat reflects hidden representations of the text tokens in thecorresponding candidate keyword as generated using a cross-tokenattention mechanism, such as the cross-token attention mechanismdescribed above in relation to a cross-token attention machine learningmodel.

The term “token-based document representation” may refer to a dataconstruct that describes a fixed-size representation of a multi-partycommunication transcript data object that is generated based at least inpart on all of the attention-based token representations for all of thetext tokens of the multi-party communication transcript data object. Insome embodiments, given a multi-party communication transcript dataobject that comprises N text tokens and is thus associated with Nattention-based token representations, the token-based documentrepresentation is generated based at least in part on (e.g., byperforming a max pooling operation on, by averaging, by concatenating,by processing using one or more trained neural network layers, and/orthe like) the N attention-based token representations. Accordingly, insome embodiments, a token-based document representation provides acomputed/predicted representation of a corresponding document dataobject that reflects hidden representations of the text tokens in thecorresponding document data object as generated using a cross-tokenattention mechanism, such as the cross-token attention mechanismdescribed above in relation to a cross-token attention machine learningmodel.

The term “document-keyword similarity score” may refer to a dataconstruct that describes a computed/predicted measure of similarity fora respective candidate keyword with respect to the multi-partycommunication transcript data object that comprises the respectivecandidate keyword. In some embodiments, the document-keyword similarityscore for a respective candidate keyword is generated based at least inpart on a computed/predicted distance measure between a token-baseddocument representation for the multi-party communication transcriptdata object in which the respective candidate keyword occurs and atoken-based keyword representation for the respective candidate keyword.In some embodiments, the document-keyword similarity score for arespective candidate keyword is generated based at least in part on acomputed/predicted normalized distance measure between a token-baseddocument representation for the multi-party communication transcriptdata object in which the respective candidate keyword occurs and atoken-based keyword representation for the respective candidate keyword.In some embodiments, the document-keyword similarity score for an ithcandidate keyword and a document data object d is generated as theoutput of the equation

${{p\left( h_{i} \right)} = \frac{R\left( h_{i} \right)}{\sum\limits_{k = 1}^{N}{R\left( h_{k} \right)}}},$where: (i) each R (h_(j)) is a non-normalized document-keywordsimilarity score for the jth candidate keyword of U candidate keywordsof d that may be calculated using the output of the equation

${R\left( h_{j} \right)} = \frac{1}{{h_{d} - h_{j}}}$(where h_(d) is the token-based document representation for d and h_(j)is the token-based keyword representation for the jth candidate keywordof U candidate keywords of d), and (ii) k is an index variable thatiterates over the U candidate keywords of d.

The term “local keyword correlation graph data object” may refer to adata construct that describes semantic correlations between candidatekeywords of a multi-party communication transcript data object, such assemantic correlations determined based at least in part on cross-tokenself-attention weights for token pairs of the multi-party communicationtranscript data object. In some embodiments, the local keywordcorrelation graph data object for a multi-party communication transcriptdata object describes a graph having nodes/vertices that correspond tocandidate keywords of the multi-party communication transcript dataobject and edges/links associated with edge/link weights that correspondto semantic correlations between candidate keyword pairs associated withnode/vertex pairs of the graph. For example, in some embodiments, givena multi-party communication transcript data object that is associatedwith K candidate keywords, the local keyword correlation graph dataobject may comprise: (i) K keyword nodes each associated with arespective keyword of the K candidate keywords, and (ii) up to K*Kkeyword correlation edges (e.g., K*K keyword correlation edges if thegraph is fully connected, less than K*K keyword correlation edges ifthose keyword correlation edges having a non-threshold-satisfyingkeyword correlation edge weight are removed from the graph, and/or thelike), where each keyword correlation edge is associated with acandidate keyword pair (i.e., a pair of candidate keywords of the Kcandidate keywords). In some embodiments, given a keyword correlationedge that is associated with a candidate keyword pair comprising a firstcandidate keyword and a second candidate keyword, the keywordcorrelation edge for the noted keyword correlation edge is generated by:(i) identifying all token pairs that relate to the keyword correlationedge such that each related token pair comprises a text token thatappears in the first candidate keyword and a text token that appears inthe second candidate keyword, and (ii) generating the keywordcorrelation edge for the noted keyword correlation edge based at leastin part on (e.g., by performing a max pooling operation on, byaveraging, by concatenating, by processing using one or more trainedneural network layers, and/or the like) all of the cross-tokenself-attention weights for the related token pairs identified in (1).For example, in an exemplary embodiment, given a first candidate keywordthat comprises N₁ text tokens and a second candidate keyword thatcomprises N₂ text tokens, then the two candidate keywords are associatedwith N₁·N₂ token pairs and thus N₁·N₂ cross-token self-attentionweights, which can then be combined (e.g., averaged) to generate thekeyword correlation edge weight for the keyword correlation edge thatconnects the keyword node for the first candidate keyword and thekeyword node for the second candidate keyword.

The term “keyword score” may refer to a data construct that describes acomputed/predicted semantic/summarization-related significance measurefor a respective candidate keyword in a respective multi-partycommunication transcript data object that is generated based at least inpart on the document-keyword similarity score for the candidate keywordand a local keyword correlation graph data object. In some embodiments,to generate the keyword scores for a set of candidate keywords of amulti-party communication transcript data object, the set of candidatekeywords are ranked by applying a page rank model/algorithm to the localkeyword correlation graph data object associated with the multi-partycommunication transcript data object, where the prior rankings of thepage rank model/algorithm are generated based at least in part on aninitial ranking determined in accordance with the document-keywordsimilarity score for the candidate keyword. In some embodiments, whilethe keyword correlation edge weights of a local keyword correlationgraph data object are generated based at least in part on cross-tokenattention weights generated using a “local” document-specific model andthus represent more “local” and document-specific measures of semanticsignificance, the document-keyword similarity scores are generated basedat least in part on distance measures generated using keywordrepresentations are thus represent more “global” andcross-document/cross-document-corpus measures of keyword semanticsignificance, and thus by using both the local keyword correlation graphdata object and the document-keyword similarity scores, a proposedsolution can use both “local” and “global” predictive inferences ingenerating keyword scores. In some embodiments, the output of the pagerank model/algorithm is a ranking of the candidate keywords, and thekeyword score for a candidate keyword is determined based at least inpart on the position of the candidate keyword within the ranking (e.g.,an inverse of the position of the candidate keyword in a descendingranking, the actual position of the candidate keyword in an ascendingranking, and/or the like).

The term “utterance” may refer to a data construct that describes acollection of one or more text tokens (e.g., words) of a multi-partycommunication transcript data object (or other document data object)that are determined (e.g., by a sentence detection model, such as asentence detection model that operates on the output of a part of speechtagger model) to constitute a semantically complete unit. In someembodiments, each utterance of a may be selected to be included as partof a general utterance summary section of an extractive summarizationfor the multi-party communication transcript data object or aparty-specific utterance summary section of an extractive summarizationfor the multi-party communication transcript data object. In someembodiments, each utterance of a multi-party communication transcriptdata object is associated with (e.g., is recorded to have been utteredby) a party profile of the P party profiles of the multi-partycommunication transcript data object. In some embodiments, eachutterance of a multi-party communication transcript data object may beassociated with an utterance-related token subset of the text tokens ofthe multi-party communication transcript data object that includes allof the tokens in the utterance. For example, if “can you still hear me?”is an utterance of the multi-party communication transcript data object,then the utterance-related token subset for the particular utterance maybe the set of text tokens {“can”, “you”, “still”, “hear”, “me”}. In someembodiments, the utterance of a multi-party communication transcriptdata object is generated by combining text tokens of the multi-partycommunication transcript data object into units based at least in parton utterance generation heuristics. For example, in some embodiments, anutterance generation heuristic may require that each consecutivesequence of text tokens of a multi-party communication transcript dataobject that appear between two dot text symbols be categorized as anutterance of the multi-party communication transcript data object.

The term “cross-utterance attention machine learning model” may refer toa data construct that describes parameters, hyperparameters, and/ordefined operations of a machine learning model that is configured togenerate, for each utterance of a set of utterances of a multi-partycommunication transcript data object, an attention-based utterancerepresentation using a cross-utterance self-attention mechanism. In someembodiments, given a set of U utterances in a multi-party communicationtranscript data object that are in turn associated with U respectiveinitial utterance representations (with each utterance being associatedwith a respective initial utterance representation), the cross-utteranceattention machine learning model: (i) for each utterance pair of U*Uutterance pairs that comprises two utterances from the multi-partycommunication transcript data object, generate a cross-utteranceself-attention weight based at least in part on the trained parametersof the cross-utterance attention machine learning model, and (ii) foreach utterance of the U utterances that is in U utterance pairs and thusis associated with U cross-utterance self-attention weights, generatethe attention-based utterance representation for the utterance byapplying a non-linear transformation to a linear combination output of afirst vector comprising the U initial utterance representations and theU cross-utterance self-attention weights for the U utterance pairs thatcomprise the particular utterance. In some of the noted embodiments, thecross-utterance attention machine learning model is itself ahierarchical attention machine learning model, such that the U initialutterance representations are themselves generated using a cross-tokenself-attention mechanism, with each initial utterance representation foran utterance u that comprises N″ text tokens is generated by: (i)receiving N^(u) initial token representations (e.g., bag of wordsrepresentations, attention-based token representations generated by thecross-token attention machine learning model, and/or the like) for theN^(u) text tokens of the utterance u as well as a default tokenrepresentation, (ii) for each token pair of N^(u)*N^(u) token pairsassociated with the N^(u) tokens of the utterance u, generating across-token self-attention weight based at least in part on the trainedparameters of the cross-utterance attention machine learning model,(iii) for each token of the 1 tokens of the utterance u that is in N^(u)token pairs and thus is associated with N^(u) cross-token self-attentionweights, generating an attention-based token representation by applyinga non-linear transformation to a linear combination output of a firstvector of the N^(u) initial token representations in the utterance u anda second vector of N^(u) cross-token attention weights for token pairsthat comprise the particular token, and (iv) combining the N^(u)attention-based token representations to generate the initial utterancerepresentation for the utterance u. Examples of hierarchical attentionmachine learning models are described in Zhang et al., HIBERT.: DocumentLevel Pre-training of Hierarchical Bidirectional Transformers forDocument Summarization (2019), arXiv:1905.06566v1 [cs.CL],https://doi.org/10.48550/arXiv.1905.06566.

The term “utterance-based document representation” may refer to a dataconstruct that describes a fixed-size representation of a multi-partycommunication transcript data object that is generated based at least inpart on all of the attention-based utterance representations for all ofthe utterances of the multi-party communication transcript data object.In some embodiments, given a multi-party communication transcript dataobject that comprises U utterances and is thus associated with Uattention-based utterance representations, the utterance-based documentrepresentation is generated based at least in part on (e.g., byperforming a max pooling operation on, by averaging, by concatenating,by processing using one or more trained neural network layers, and/orthe like) the U attention-based utterance representations. Accordingly,in some embodiments, an utterance-based document representation providesa computed/predicted representation of a corresponding document dataobject that reflects hidden representations of the utterances in thecorresponding document data object as generated using a cross-utteranceattention mechanism, such as the cross-utterance attention mechanismdescribed above in relation to a cross-utterance attention machinelearning model.

The term “local utterance correlation graph data object” may refer to adata construct that describes semantic correlations between utterancesof a multi-party communication transcript data object, such as semanticcorrelations determined based at least in part on cross-utteranceself-attention weights for utterance pairs of the multi-partycommunication transcript data object. In some embodiments, the localutterance correlation graph data object for a multi-party communicationtranscript data object describes a graph having nodes/vertices thatcorrespond to utterances of the multi-party communication transcriptdata object and edges/links associated with edge/link weights thatcorrespond to semantic correlations between utterance pairs associatedwith node/vertex pairs of the graph. For example, in some embodiments,given a multi-party communication transcript data object that isassociated with U utterances, the local utterance correlation graph dataobject may comprise: (i) U utterance nodes each associated with arespective utterance of the U utterances, and (ii) up to U*U utterancecorrelation edges (e.g., U*U utterance correlation edges if the graph isfully connected, less than U*U utterance correlation edges if thoseutterance correlation edges having a non-threshold-satisfying utterancecorrelation edge weight are removed from the graph, and/or the like),where each utterance correlation edge is associated with an utterancepair (i.e., a pair of utterances of the U utterances). In someembodiments, given an utterance correlation edge that is associated withan utterance pair comprising a first utterance and a second utterance,the utterance correlation edge for the noted utterance correlation edgeis generated based at least in part on the cross-utteranceself-attention weight for the utterance pair as generated by thecross-utterance attention machine learning model.

The term “integer linear programming joint keyword-utteranceoptimization model” may refer to a data construct that describesparameters, hyperparameters, and/or defined operations of a model thatis configured to generate a selected utterance subset of the utterancesof the multi-party communication transcript data object and a selectedkeyword subset of the candidate keywords of the multi-partycommunication transcript data object in a manner that is configured tomaximize a joint keyword-utterance score for the selected utterancesubset and the selected keyword subset given one or more integer linearprogramming optimization constraints. In some embodiments, the integerlinear programming optimization constraints of the integer linearprogramming joint keyword-utterance optimization model comprise at leastone of a party utterance summary length constraint requiring that eachparty utterance summary satisfies an upper-bound party utterance summarylength threshold, a keyword-based utterance coverage constraintrequiring that, if the selected keyword subset comprises a particularcandidate keyword, the selected utterance subset comprises at least oneutterance that comprises the particular candidate keyword, anutterance-based keyword coverage constraint requiring that, if theselected utterance subset comprises a particular candidate keyword, theselected keyword subset comprises the particular candidate keyword, anutterance non-emptiness constraint requiring that, for each partyprofile, the selected utterance subset comprises at least one utterancerelated to the party profile, a pairwise utterance selection constraintrequiring that, if a pairwise utterance similarity score for across-party utterance pair comprising a first utterance from a firstparty profile and a second utterance from a different party profilesatisfies a lower-bound pairwise utterance similarity threshold, theselected utterance subset comprises both the first utterance and thesecond utterance, or a keyword summary length constraint requiring thata selected keyword count of the selected keyword subset satisfies anupper-bound keyword selection count threshold.

The term “joint keyword-utterance score” may refer to a data constructthat describes, for the combination of a set of utterances and a set ofkeywords, a relevance/selection score for the two sets that is generatedbased at least in part on the utterances scores for the set ofutterances and the keyword scores for the set of keywords. For example,given an ith candidate selected keyword subset comprising K_(si)candidate keywords that are associated with K_(si) keyword scoresrespectively as well as a jth candidate selected utterance subsetcomprising U_(sj) utterances that are associated with U utterance scoresrespectively, the joint keyword-utterance score for the combination ofthe ith candidate selected keyword subset and the a jth candidateselected utterance subset may be generated based at least in part on theK_(si) keyword scores and the U_(sj) utterance scores. In someembodiments, the joint keyword-utterance score for an ith candidateselected keyword subset and a jth candidate selected utterance subset isgenerated based at least in part on the output of the equation

${JS}_{i,j} = {{\sum\limits_{p = 1}^{2}{\sum\limits_{u = 1}^{U_{p}}{{{US}\left( U_{u}^{j} \right)} \star X_{u}^{j}}}} + {\sum\limits_{k = 1}^{K}{{{KS}(k)} \star {Y_{k}.}}}}$In this equation: (i) p is an index variable that iterates over two (or,in some embodiments, more than two) party profiles of the multi-partycommunication transcript data object, (ii) u is an index variable thatiterates over U_(p) utterances of the pth party profile, (iii) U_(u)^(j) is the uth utterance of the U_(p) utterances of the pth partyprofile, (iv) US(U_(u) ^(j)) is the utterance score (e.g., as determinedbased at least in part on utterance informativeness and/or ranking) ofU_(u) ^(j)(v) X_(u) ^(j) is a binary variable that is set to one ifU_(u) ^(j) is part of the jth candidate selected utterance subset and isset to zero otherwise (and is thus different across different utterancesets, allowing the resulting joint keyword-utterance score to change asthe utterance sets change), (vi) k is an index variables that iteratesover K candidate keywords of the multi-party communication transcriptdata object, (vi) KS (k) is the keyword score (e.g., as determined basedat least in part on the keyword ranking) for the kth candidate keywordof the K candidate keywords of the multi-party communication transcriptdata object, and (vii) Y_(k) is a binary variable that is set to one ifthe kth candidate keyword is part of the ith candidate selected keywordsubset and is set to zero otherwise (and is thus different acrossdifferent keyword sets, allowing the resulting joint keyword-utterancescore to change as the keyword sets change).

The term “party utterance summary length constraint” may refer to a dataconstruct that describes an integer linear programming optimizationconstraint requiring that each party utterance summary satisfies anupper-bound party utterance summary length threshold. In other words,the party utterance summary length constraint requires that, for eachparty, the number of utterances of the party that are included in theselected utterance subset (and are thus in the party utterance summaryfor the party) satisfy (e.g., fall below) an upper-bound party utterancesummary length threshold (which may be a party-specific value or may bea common value across all parties). In general, any upper-boundthreshold described in this application may be satisfied by a value ifthe value falls below the upper-bound threshold or falls below or isequal to the upper-bound threshold, depending on the embodiment. In someembodiments, the party utterance summary length constraint does notallow selection of a selected utterance subset that comprises a numberof utterances of even one-party profile that is above an upper boundthreshold. In some embodiments, the party utterance summary lengthconstraint does not allow a selected utterance subset that comprises anumber of utterances of even one-party profile that is above or equal toan upper bound threshold.

The term “keyword-based utterance coverage constraint” may refer to adata construct that describes an integer linear programming optimizationconstraint requiring that, if the selected keyword subset comprises aparticular candidate keyword, the selected utterance subset comprises atleast one utterance that comprises the particular candidate keyword. Inother words, the keyword-based utterance coverage constraint requiresthat each candidate keyword in the selected keyword subset be includedin at least one utterance of the selected utterance subset. Accordingly,given a combination of a candidate selected keyword subset and acandidate selected utterance subset, if the candidate selected keywordsubset comprises even one candidate keyword that is not part of anyutterances that are in the candidate selected utterance subset, then thecombination cannot be selected as the selected keyword subset and theselected utterance subset according to the keyword-based utterancecoverage constraint.

The term “utterance-based keyword coverage constraint” may refer to adata construct that describes an integer linear programming optimizationconstraint requiring that, if the selected utterance subset comprises aparticular candidate keyword, the selected keyword subset comprises theparticular candidate keyword. In other words, the utterance-basedkeyword coverage constraint requires that all candidate keywords thatappear in utterances of the selected utterance subset be in the selectedkeyword subset. Accordingly, given a combination of a candidate selectedkeyword subset and a candidate selected utterance subset, if even onecandidate keyword that appears in even one utterance of the candidateselected utterance subset is not part of the candidate selected keywordsubset, then the combination cannot be selected as the selected keywordsubset and the selected utterance subset according to theutterance-based keyword coverage constraint.

The term “pairwise utterance selection constraint” may refer to a dataconstruct that describes an integer linear programming optimizationconstraint requiring that, if a pairwise utterance similarity score for(e.g., a cosine similarity score of the respective attention-basedutterance representations of) a cross-party utterance pair comprising afirst utterance from a first party profile and a second utterance from adifferent party profile satisfies a lower-bound pairwise utterancesimilarity threshold, the selected utterance subset comprises both thefirst utterance and the second utterance. In other words, given a firstparty profile associated with a multi-party communication transcriptdata object that is associated with (e.g., is recorded to be thespeaker/utterer of) U¹ utterances of the multi-party communicationtranscript data object as well as a second, different party profileassociated with the multi-party communication transcript data objectthat is associated with (e.g., is recorded to be the speaker/utterer of)U² utterances of the multi-party communication transcript data object,and thus given U¹*U² cross-party utterance pairs each comprising one ofthe U¹ utterances of the first party profile and one of the U²utterances of the second party profile, then for each particularcross-party utterance pair that comprises a first utterance of the U¹utterances of the first party profile and a second utterance of the U²utterances of the second party profile, the following operations areperformed: (i) a pairwise utterance similarity score for the particularcross-party utterance pair is generated, for example based at least inpart on a similarity score (e.g., a cosine similarity score) between theattention-based utterance representation of the first utterance and theattention-based utterance representation of the second utterance, (ii) adetermination is made about whether the pairwise utterance similarityscore for the particular cross-party utterance pair satisfies (e.g.,falls above, falls above or is equal to, and/or the like) a lower-boundpairwise utterance similarity threshold, and (iii) if the determinationat (ii) shows that the pairwise utterance similarity score for theparticular cross-party utterance pair satisfies (e.g., falls above,falls above or is equal to, and/or the like) the lower-bound pairwiseutterance similarity threshold, then the pairwise utterance selectionconstraint requires that the first utterance and the second utterance beboth added to the selected utterance subset. In general, any lower-boundthreshold described in this application may be satisfied by a value ifthe value falls above the lower-bound threshold or falls above or isequal to the lower-bound threshold, depending on the embodiment.

The term “pairwise lower-bound utterance similarity threshold” may referto a data construct that describes a statistical distribution measure ofpairwise utterance similarity scores for cross-party utterance pairs ofa two-party communication transcript data object. In some embodiments,the pairwise lower-bound utterance similarity threshold is generatedbased at least in part on a deviation measure between: (i) a maximalpairwise utterance similarity score for all cross-party utterance pairs,and (ii) a predefined maximal pairwise utterance similarity scoredeviation threshold. In other words, given a first party profileassociated with a multi-party communication transcript data object thatis associated with (e.g., is recorded to be the speaker/utterer of) U¹utterances of the multi-party communication transcript data object aswell as a second, different party profile associated with themulti-party communication transcript data object that is associated with(e.g., is recorded to be the speaker/utterer of) U² utterances of themulti-party communication transcript data object, and thus given U¹*U²cross-party utterance pairs each comprising one of the U¹ utterances ofthe first party profile and one of the U² utterances of the second partyprofile, to generate the pairwise lower-bound utterance similaritythreshold the following operations are performed: (i) for eachcross-party utterance pair that comprises a first utterance of the U¹utterances of the first party profile and a second utterance of the U²utterances of the second party profile, a pairwise utterance similarityscore is generated, thus resulting in U¹*U² pairwise utterancesimilarity scores, (ii) the highest pairwise utterance similarity scoreof the U¹*U² pairwise utterance similarity scores is detected, (iii) thepairwise lower-bound utterance similarity threshold is generated basedat least in part on the highest pairwise utterance similarity scoreminus a predefined value known as a predefined maximal pairwiseutterance similarity score deviation threshold, which may be defined byconfiguration data associated with a corresponding predictive dataanalysis system.

III. COMPUTER PROGRAM PRODUCTS, METHODS, AND COMPUTING ENTITIES

Embodiments of the present invention may be implemented in various ways,including as computer program products that comprise articles ofmanufacture. Such computer program products may include one or moresoftware components including, for example, software objects, methods,data structures, or the like. A software component may be coded in anyof a variety of programming languages. An illustrative programminglanguage may be a lower-level programming language such as an assemblylanguage associated with a particular hardware architecture and/oroperating system platform. A software component comprising assemblylanguage instructions may require conversion into executable machinecode by an assembler prior to execution by the hardware architectureand/or platform. Another example programming language may be ahigher-level programming language that may be portable across multiplearchitectures. A software component comprising higher-level programminglanguage instructions may require conversion to an intermediaterepresentation by an interpreter or a compiler prior to execution.

Other examples of programming languages include, but are not limited to,a macro language, a shell or command language, a job control language, ascript language, a database query or search language, and/or a reportwriting language. In one or more example embodiments, a softwarecomponent comprising instructions in one of the foregoing examples ofprogramming languages may be executed directly by an operating system orother software component without having to be first transformed intoanother form. A software component may be stored as a file or other datastorage construct. Software components of a similar type or functionallyrelated may be stored together such as, for example, in a particulardirectory, folder, or library. Software components may be static (e.g.,pre-established or fixed) or dynamic (e.g., created or modified at thetime of execution).

A computer program product may include a non-transitorycomputer-readable storage medium storing applications, programs, programmodules, scripts, source code, program code, object code, byte code,compiled code, interpreted code, machine code, executable instructions,and/or the like (also referred to herein as executable instructions,instructions for execution, computer program products, program code,and/or similar terms used herein interchangeably). Such non-transitorycomputer-readable storage media include all computer-readable media(including volatile and non-volatile media).

In one embodiment, a non-volatile computer-readable storage medium mayinclude a floppy disk, flexible disk, hard disk, solid-state storage(SSS) (e.g., a solid state drive (SSD), solid state card (SSC), solidstate module (SSM), enterprise flash drive, magnetic tape, or any othernon-transitory magnetic medium, and/or the like. A non-volatilecomputer-readable storage medium may also include a punch card, papertape, optical mark sheet (or any other physical medium with patterns ofholes or other optically recognizable indicia), compact disc read onlymemory (CD-ROM), compact disc-rewritable (CD-RW), digital versatile disc(DVD), Blu-ray disc (BD), any other non-transitory optical medium,and/or the like. Such a non-volatile computer-readable storage mediummay also include read-only memory (ROM), programmable read-only memory(PROM), erasable programmable read-only memory (EPROM), electricallyerasable programmable read-only memory (EEPROM), flash memory (e.g.,Serial, NAND, NOR, and/or the like), multimedia memory cards (MMC),secure digital (SD) memory cards, SmartMedia cards, CompactFlash (CF)cards, Memory Sticks, and/or the like. Further, a non-volatilecomputer-readable storage medium may also include conductive-bridgingrandom access memory (CBRAM), phase-change random access memory (PRAM),ferroelectric random-access memory (FeRAM), non-volatile random-accessmemory (NVRAM), magnetoresistive random-access memory (MRAM), resistiverandom-access memory (RRAM), Silicon-Oxide-Nitride-Oxide-Silicon memory(SONOS), floating junction gate random access memory (FJG RAM),Millipede memory, racetrack memory, and/or the like.

In one embodiment, a volatile computer-readable storage medium mayinclude random access memory (RAM), dynamic random access memory (DRAM),static random access memory (SRAM), fast page mode dynamic random accessmemory (FPM DRAM), extended data-out dynamic random access memory (EDODRAM), synchronous dynamic random access memory (SDRAM), double datarate synchronous dynamic random access memory (DDR SDRAM), double datarate type two synchronous dynamic random access memory (DDR2 SDRAM),double data rate type three synchronous dynamic random access memory(DDR3 SDRAM), Rambus dynamic random access memory (RDRAM), TwinTransistor RAM (TTRAM), Thyristor RAM (T-RAM), Zero-capacitor (Z-RAM),Rambus in-line memory module (RIMM), dual in-line memory module (DIMM),single in-line memory module (SIMM), video random access memory (VRAM),cache memory (including various levels), flash memory, register memory,and/or the like. It will be appreciated that where embodiments aredescribed to use a computer-readable storage medium, other types ofcomputer-readable storage media may be substituted for or used inaddition to the computer-readable storage media described above.

As should be appreciated, various embodiments of the present inventionmay also be implemented as methods, apparatus, systems, computingdevices, computing entities, and/or the like. As such, embodiments ofthe present invention may take the form of an apparatus, system,computing device, computing entity, and/or the like executinginstructions stored on a computer-readable storage medium to performcertain steps or operations. Thus, embodiments of the present inventionmay also take the form of an entirely hardware embodiment, an entirelycomputer program product embodiment, and/or an embodiment that comprisescombination of computer program products and hardware performing certainsteps or operations. Embodiments of the present invention are describedbelow with reference to block diagrams and flowchart illustrations.Thus, it should be understood that each block of the block diagrams andflowchart illustrations may be implemented in the form of a computerprogram product, an entirely hardware embodiment, a combination ofhardware and computer program products, and/or apparatus, systems,computing devices, computing entities, and/or the like carrying outinstructions, operations, steps, and similar words used interchangeably(e.g., the executable instructions, instructions for execution, programcode, and/or the like) on a computer-readable storage medium forexecution. For example, retrieval, loading, and execution of code may beperformed sequentially such that one instruction is retrieved, loaded,and executed at a time. In some exemplary embodiments, retrieval,loading, and/or execution may be performed in parallel such thatmultiple instructions are retrieved, loaded, and/or executed together.Thus, such embodiments can produce specifically-configured machinesperforming the steps or operations specified in the block diagrams andflowchart illustrations. Accordingly, the block diagrams and flowchartillustrations support various combinations of embodiments for performingthe specified instructions, operations, or steps.

IV. EXEMPLARY SYSTEM ARCHITECTURE

FIG. 1 is a schematic diagram of an example architecture 100 forperforming predictive data analysis. The architecture 100 includes apredictive data analysis system 101 configured to receive predictivedata analysis requests from client computing entities 102, process thepredictive data analysis requests to generate predictions, provide thegenerated predictions to the client computing entities 102, andautomatically perform prediction-based actions based at least in part onthe generated predictions. An example of a prediction-based action thatcan be performed using the predictive data analysis system 101 is arequest for generating an extractive summarization for a call transcriptdocument.

In some embodiments, predictive data analysis system 101 may communicatewith at least one of the client computing entities 102 using one or morecommunication networks. Examples of communication networks include anywired or wireless communication network including, for example, a wiredor wireless local area network (LAN), personal area network (PAN),metropolitan area network (MAN), wide area network (WAN), or the like,as well as any hardware, software and/or firmware required to implementit (such as, e.g., network routers, and/or the like).

The predictive data analysis system 101 may include a predictive dataanalysis computing entity 106 and a storage subsystem 108. Thepredictive data analysis computing entity 106 may be configured toreceive predictive data analysis requests from one or more clientcomputing entities 102, process the predictive data analysis requests togenerate predictions corresponding to the predictive data analysisrequests, provide the generated predictions to the client computingentities 102, and automatically perform prediction-based actions basedat least in part on the generated predictions.

The storage subsystem 108 may be configured to store input data used bythe predictive data analysis computing entity 106 to perform predictivedata analysis as well as model definition data used by the predictivedata analysis computing entity 106 to perform various predictive dataanalysis tasks. The storage subsystem 108 may include one or morestorage units, such as multiple distributed storage units that areconnected through a computer network. Each storage unit in the storagesubsystem 108 may store at least one of one or more data assets and/orone or more data about the computed properties of one or more dataassets. Moreover, each storage unit in the storage subsystem 108 mayinclude one or more non-volatile storage or memory media including, butnot limited to, hard disks, ROM, PROM, EPROM, EEPROM, flash memory,MMCs, SD memory cards, Memory Sticks, CBRAM, PRAM, FeRAM, NVRAM, MRAM,RRAM, SONOS, FJG RAM, Millipede memory, racetrack memory, and/or thelike.

A. Exemplary Predictive Data Analysis Computing Entity

FIG. 2 provides a schematic of a predictive data analysis computingentity 106 according to one embodiment of the present invention. Ingeneral, the terms computing entity, computer, entity, device, system,and/or similar words used herein interchangeably may refer to, forexample, one or more computers, computing entities, desktops, mobilephones, tablets, phablets, notebooks, laptops, distributed systems,kiosks, input terminals, servers or server networks, blades, gateways,switches, processing devices, processing entities, set-top boxes,relays, routers, network access points, base stations, the like, and/orany combination of devices or entities adapted to perform the functions,operations, and/or processes described herein. Such functions,operations, and/or processes may include, for example, transmitting,receiving, operating on, processing, displaying, storing, determining,creating/generating, monitoring, evaluating, comparing, and/or similarterms used herein interchangeably. In one embodiment, these functions,operations, and/or processes can be performed on data, content,information, and/or similar terms used herein interchangeably.

As indicated, in one embodiment, the predictive data analysis computingentity 106 may also include one or more communications interfaces 220for communicating with various computing entities, such as bycommunicating data, content, information, and/or similar terms usedherein interchangeably that can be transmitted, received, operated on,processed, displayed, stored, and/or the like.

As shown in FIG. 2 , in one embodiment, the predictive data analysiscomputing entity 106 may include, or be in communication with, one ormore processing elements 205 (also referred to as processors, processingcircuitry, and/or similar terms used herein interchangeably) thatcommunicate with other elements within the predictive data analysiscomputing entity 106 via a bus, for example. As will be understood, theprocessing element 205 may be embodied in a number of different ways.

For example, the processing element 205 may be embodied as one or morecomplex programmable logic devices (CPLDs), microprocessors, multi-coreprocessors, coprocessing entities, application-specific instruction-setprocessors (ASIPs), microcontrollers, and/or controllers. Further, theprocessing element 205 may be embodied as one or more other processingdevices or circuitry. The term circuitry may refer to an entirelyhardware embodiment or a combination of hardware and computer programproducts. Thus, the processing element 205 may be embodied as integratedcircuits, application specific integrated circuits (ASICs), fieldprogrammable gate arrays (FPGAs), programmable logic arrays (PLAs),hardware accelerators, other circuitry, and/or the like.

As will therefore be understood, the processing element 205 may beconfigured for a particular use or configured to execute instructionsstored in volatile or non-volatile media or otherwise accessible to theprocessing element 205. As such, whether configured by hardware orcomputer program products, or by a combination thereof, the processingelement 205 may be capable of performing steps or operations accordingto embodiments of the present invention when configured accordingly.

In one embodiment, the predictive data analysis computing entity 106 mayfurther include, or be in communication with, non-volatile media (alsoreferred to as non-volatile storage, memory, memory storage, memorycircuitry and/or similar terms used herein interchangeably). In oneembodiment, the non-volatile storage or memory may include one or morenon-volatile storage or memory media 210, including, but not limited to,hard disks, ROM, PROM, EPROM, EEPROM, flash memory, MMCs, SD memorycards, Memory Sticks, CBRAM, PRAM, FeRAM, NVRAM, MRAM, RRAM, SONOS, FJGRAM, Millipede memory, racetrack memory, and/or the like.

As will be recognized, the non-volatile storage or memory media maystore databases, database instances, database management systems, data,applications, programs, program modules, scripts, source code, objectcode, byte code, compiled code, interpreted code, machine code,executable instructions, and/or the like. The term database, databaseinstance, database management system, and/or similar terms used hereininterchangeably may refer to a collection of records or data that isstored in a computer-readable storage medium using one or more databasemodels, such as a hierarchical database model, network model, relationalmodel, entity— relationship model, object model, document model,semantic model, graph model, and/or the like.

In one embodiment, the predictive data analysis computing entity 106 mayfurther include, or be in communication with, volatile media (alsoreferred to as volatile storage, memory, memory storage, memorycircuitry and/or similar terms used herein interchangeably). In oneembodiment, the volatile storage or memory may also include one or morevolatile storage or memory media 215, including, but not limited to,RAM, DRAM, SRAM, FPM DRAM, EDO DRAM, SDRAM, DDR SDRAM, DDR2 SDRAM, DDR3SDRAM, RDRAM, TTRAM, T-RAM, Z-RAM, RIMM, DIMM, SIMM, VRAM, cache memory,register memory, and/or the like.

As will be recognized, the volatile storage or memory media may be usedto store at least portions of the databases, database instances,database management systems, data, applications, programs, programmodules, scripts, source code, object code, byte code, compiled code,interpreted code, machine code, executable instructions, and/or the likebeing executed by, for example, the processing element 205. Thus, thedatabases, database instances, database management systems, data,applications, programs, program modules, scripts, source code, objectcode, byte code, compiled code, interpreted code, machine code,executable instructions, and/or the like may be used to control certainaspects of the operation of the predictive data analysis computingentity 106 with the assistance of the processing element 205 andoperating system.

As indicated, in one embodiment, the predictive data analysis computingentity 106 may also include one or more communications interfaces 220for communicating with various computing entities, such as bycommunicating data, content, information, and/or similar terms usedherein interchangeably that can be transmitted, received, operated on,processed, displayed, stored, and/or the like. Such communication may beexecuted using a wired data transmission protocol, such as fiberdistributed data interface (FDDI), digital subscriber line (DSL),Ethernet, asynchronous transfer mode (ATM), frame relay, data over cableservice interface specification (DOCSIS), or any other wiredtransmission protocol. Similarly, the predictive data analysis computingentity 106 may be configured to communicate via wireless externalcommunication networks using any of a variety of protocols, such asgeneral packet radio service (GPRS), Universal Mobile TelecommunicationsSystem (UMTS), Code Division Multiple Access 2000 (CDMA2000), CDMA20001× (1×RTT), Wideband Code Division Multiple Access (WCDMA), GlobalSystem for Mobile Communications (GSM), Enhanced Data rates for GSMEvolution (EDGE), Time Division-Synchronous Code Division MultipleAccess (TD-SCDMA), Long Term Evolution (LTE), Evolved UniversalTerrestrial Radio Access Network (E-UTRAN), Evolution-Data Optimized(EVDO), High Speed Packet Access (HSPA), High-Speed Downlink PacketAccess (HSDPA), IEEE 802.11 (Wi-Fi), Wi-Fi Direct, 802.16 (WiMAX),ultra-wideband (UWB), infrared (IR) protocols, near field communication(NFC) protocols, Wibree, Bluetooth protocols, wireless universal serialbus (USB) protocols, and/or any other wireless protocol.

Although not shown, the predictive data analysis computing entity 106may include, or be in communication with, one or more input elements,such as a keyboard input, a mouse input, a touch screen/display input,motion input, movement input, audio input, pointing device input,joystick input, keypad input, and/or the like. The predictive dataanalysis computing entity 106 may also include, or be in communicationwith, one or more output elements (not shown), such as audio output,video output, screen/display output, motion output, movement output,and/or the like.

B. Exemplary Client Computing Entity

FIG. 3 provides an illustrative schematic representative of a clientcomputing entity 102 that can be used in conjunction with embodiments ofthe present invention. In general, the terms device, system, computingentity, entity, and/or similar words used herein interchangeably mayrefer to, for example, one or more computers, computing entities,desktops, mobile phones, tablets, phablets, notebooks, laptops,distributed systems, kiosks, input terminals, servers or servernetworks, blades, gateways, switches, processing devices, processingentities, set-top boxes, relays, routers, network access points, basestations, the like, and/or any combination of devices or entitiesadapted to perform the functions, operations, and/or processes describedherein. Client computing entities 102 can be operated by variousparties. As shown in FIG. 3 , the client computing entity 102 caninclude an antenna 312, a transmitter 304 (e.g., radio), a receiver 306(e.g., radio), and a processing element 308 (e.g., CPLDs,microprocessors, multi-core processors, coprocessing entities, ASIPs,microcontrollers, and/or controllers) that provides signals to andreceives signals from the transmitter 304 and receiver 306,correspondingly.

The signals provided to and received from the transmitter 304 and thereceiver 306, correspondingly, may include signaling information/data inaccordance with air interface standards of applicable wireless systems.In this regard, the client computing entity 102 may be capable ofoperating with one or more air interface standards, communicationprotocols, modulation types, and access types. More particularly, theclient computing entity 102 may operate in accordance with any of anumber of wireless communication standards and protocols, such as thosedescribed above with regard to the predictive data analysis computingentity 106. In a particular embodiment, the client computing entity 102may operate in accordance with multiple wireless communication standardsand protocols, such as UMTS, CDMA2000, 1×RTT, WCDMA, GSM, EDGE,TD-SCDMA, LTE, E-UTRAN, EVDO, HSPA, HSDPA, Wi-Fi, Wi-Fi Direct, WiMAX,UWB, IR, NFC, Bluetooth, USB, and/or the like. Similarly, the clientcomputing entity 102 may operate in accordance with multiple wiredcommunication standards and protocols, such as those described abovewith regard to the predictive data analysis computing entity 106 via anetwork interface 320.

Via these communication standards and protocols, the client computingentity 102 can communicate with various other entities using conceptssuch as Unstructured Supplementary Service Data (USSD), Short MessageService (SMS), Multimedia Messaging Service (MMS), Dual-ToneMulti-Frequency Signaling (DTMF), and/or Subscriber Identity ModuleDialer (SIM dialer). The client computing entity 102 can also downloadchanges, add-ons, and updates, for instance, to its firmware, software(e.g., including executable instructions, applications, programmodules), and operating system.

According to one embodiment, the client computing entity 102 may includelocation determining aspects, devices, modules, functionalities, and/orsimilar words used herein interchangeably. For example, the clientcomputing entity 102 may include outdoor positioning aspects, such as alocation module adapted to acquire, for example, latitude, longitude,altitude, geocode, course, direction, heading, speed, universal time(UTC), date, and/or various other information/data. In one embodiment,the location module can acquire data, sometimes known as ephemeris data,by identifying the number of satellites in view and the relativepositions of those satellites (e.g., using global positioning systems(GPS)). The satellites may be a variety of different satellites,including Low Earth Orbit (LEO) satellite systems, Department of Defense(DOD) satellite systems, the European Union Galileo positioning systems,the Chinese Compass navigation systems, Indian Regional Navigationalsatellite systems, and/or the like. This data can be collected using avariety of coordinate systems, such as the Decimal Degrees (DD);Degrees, Minutes, Seconds (DMS); Universal Transverse Mercator (UTM);Universal Polar Stereographic (UPS) coordinate systems; and/or the like.Alternatively, the location information/data can be determined bytriangulating the client computing entity's 102 position in connectionwith a variety of other systems, including cellular towers, Wi-Fi accesspoints, and/or the like. Similarly, the client computing entity 102 mayinclude indoor positioning aspects, such as a location module adapted toacquire, for example, latitude, longitude, altitude, geocode, course,direction, heading, speed, time, date, and/or various otherinformation/data. Some of the indoor systems may use various position orlocation technologies including RFID tags, indoor beacons ortransmitters, Wi-Fi access points, cellular towers, nearby computingdevices (e.g., smartphones, laptops) and/or the like. For instance, suchtechnologies may include the iBeacons, Gimbal proximity beacons,Bluetooth Low Energy (BLE) transmitters, NFC transmitters, and/or thelike. These indoor positioning aspects can be used in a variety ofsettings to determine the location of someone or something to withininches or centimeters.

The client computing entity 102 may also comprise a user interface (thatcan include a display 316 coupled to a processing element 308) and/or auser input interface (coupled to a processing element 308). For example,the user interface may be a user application, browser, user interface,and/or similar words used herein interchangeably executing on and/oraccessible via the client computing entity 102 to interact with and/orcause display of information/data from the predictive data analysiscomputing entity 106, as described herein. The user input interface cancomprise any of a number of devices or interfaces allowing the clientcomputing entity 102 to receive data, such as a keypad 318 (hard orsoft), a touch display, voice/speech or motion interfaces, or otherinput device. In embodiments including a keypad 318, the keypad 318 caninclude (or cause display of) the conventional numeric (0-9) and relatedkeys (#, *), and other keys used for operating the client computingentity 102 and may include a full set of alphabetic keys or set of keysthat may be activated to provide a full set of alphanumeric keys. Inaddition to providing input, the user input interface can be used, forexample, to activate or deactivate certain functions, such as screensavers and/or sleep modes.

The client computing entity 102 can also include volatile storage ormemory 322 and/or non-volatile storage or memory 324, which can beembedded and/or may be removable. For example, the non-volatile memorymay be ROM, PROM, EPROM, EEPROM, flash memory, MMCs, SD memory cards,Memory Sticks, CBRAM, PRAM, FeRAM, NVRAM, MRAM, RRAM, SONOS, FJG RAM,Millipede memory, racetrack memory, and/or the like. The volatile memorymay be RAM, DRAM, SRAM, FPM DRAM, EDO DRAM, SDRAM, DDR SDRAM, DDR2SDRAM, DDR3 SDRAM, RDRAM, TTRAM, T-RAM, Z-RAM, RIMM, DIMM, SIMM, VRAM,cache memory, register memory, and/or the like. The volatile andnon-volatile storage or memory can store databases, database instances,database management systems, data, applications, programs, programmodules, scripts, source code, object code, byte code, compiled code,interpreted code, machine code, executable instructions, and/or the liketo implement the functions of the client computing entity 102. Asindicated, this may include a user application that is resident on theentity or accessible through a browser or other user interface forcommunicating with the predictive data analysis computing entity 106and/or various other computing entities.

In another embodiment, the client computing entity 102 may include oneor more components or functionality that are the same or similar tothose of the predictive data analysis computing entity 106, as describedin greater detail above. As will be recognized, these architectures anddescriptions are provided for exemplary purposes only and are notlimiting to the various embodiments.

In various embodiments, the client computing entity 102 may be embodiedas an artificial intelligence (AI) computing entity, such as an AmazonEcho, Amazon Echo Dot, Amazon Show, Google Home, and/or the like.Accordingly, the client computing entity 102 may be configured toprovide and/or receive information/data from a user via an input/outputmechanism, such as a display, a camera, a speaker, a voice-activatedinput, and/or the like. In certain embodiments, an AI computing entitymay comprise one or more predefined and executable program algorithmsstored within an onboard memory storage module, and/or accessible over anetwork. In various embodiments, the AI computing entity may beconfigured to retrieve and/or execute one or more of the predefinedprogram algorithms upon the occurrence of a predefined trigger event.

V. EXEMPLARY SYSTEM OPERATIONS

As described below, various embodiments of the present inventiondisclose techniques for improving storage efficiency of document storagesystems. As described herein, various embodiments of the presentinvention disclose techniques for generating extractive summarizationsof document data objects that comprise a selected utterance subset ofthe utterances of each document data object and a selected keywordsubset of the candidate keywords of each document data object. Becausean extractive summarization of a document data object is smaller in sizethan the underlying document data object (as the extractivesummarization includes subsets of candidate keywords and utterancesdescribed by the document data object), various embodiments of thepresent invention enable storing extractive summarizations of documentdata objects instead of the document data objects that are bigger insize. In this way, various embodiments of the present invention reducestorage requirements associated with storing document data, and thusincrease storage efficiency of storing document data associated withdocument data objects. Accordingly, by generating extractivesummarizations of document data objects that comprise a selectedutterance subset of the utterances of each document data object and aselected keyword subset of the candidate keywords of each document dataobject, various embodiments of the present invention disclose techniquesfor improving storage efficiency of various document storage systems.

Moreover, as further described below, various embodiments of the presentinvention make important technical contributions to improving predictiveaccuracy of natural language processing machine learning models that areconfigured to perform natural language processing operations on documentdata objects by using an integer linear programming jointkeyword-utterance optimization model that generates a selected utterancesubset of the utterances of a document data object and a selectedkeyword subset of the candidate keywords of the document data object ina manner that is configured to maximize a joint keyword-utterance scorefor the selected utterance subset and the selected keyword subset givenone or more integer linear programming optimization constraints, anapproach which in turn improves training speed and training efficiencyof training the noted natural language processing machine learningmodels. It is well-understood in the relevant art that there istypically a tradeoff between predictive accuracy and training speed,such that it is trivial to improve training speed by reducing predictiveaccuracy, and thus the real challenge is to improve training speedwithout sacrificing predictive accuracy through innovative modelarchitectures, see, e.g., Sun et al., Feature-Frequency—Adaptive On-lineTraining for Fast and Accurate Natural Language Processing in 40(3)Computational Linguistic 563 at Abst. (“Typically, we need to make atradeoff between speed and accuracy. It is trivial to improve thetraining speed via sacrificing accuracy or to improve the accuracy viasacrificing speed. Nevertheless, it is nontrivial to improve thetraining speed and the accuracy at the same time”). Accordingly,techniques that improve predictive accuracy without harming trainingspeed, such as the techniques described herein, enable improvingtraining speed given a constant predictive accuracy. In doing so, thetechniques described herein improving efficiency and speed of trainingnatural language processing machine learning models, thus reducing thenumber of computational operations needed and/or the amount of trainingdata entries needed to train natural language processing machinelearning models. Accordingly, the techniques described herein improve atleast one of the computational efficiency, storage-wise efficiency, andspeed of training natural language processing machine learning models.

FIG. 4 is a flowchart diagram of an example process 400 for generatingan extractive summarization for a multi-party communication transcriptdata object that is associated with two party profiles (e.g., a callerparty profile and an agent profile). Via the various steps/operations ofthe process 400, the predictive data analysis computing entity 106 canuse a combination of a cross-token attention machine learning, across-utterance attention machine learning model, and an integer linearprogramming joint keyword-utterance optimization model to select anextractive keyword summarization of a multi-party communicationtranscript data object that comprises a selected utterance subset of Uutterances (e.g., U sentences) of the multi-party communicationtranscript data object and a selected keyword subset of K candidatekeywords of the multi-party communication transcript data object.

The process 400 begins at step/operation 401 when the predictive dataanalysis computing entity 106 generates, for each candidate keyword ofthe multi-party communication transcript data object, a keyword score.An example of a multi-party communication transcript data object is acall transcript data object, such as a call transcript data object thatcomprises transcribed text data associated with utterances of two-partyprofiles (e.g., a party profile associated with a call center agentprofile and a party profile associated with a caller/service requesterparty). An operational example of a multi-party communication transcriptdata object 500 that comprises utterances from two party profilesassociated with two communication parties is depicted in FIG. 5 .

In some embodiments, a candidate keyword is a collection of one or moretext tokens (e.g., words) of a multi-party communication transcript dataobject (or other document data object), where the candidate keywords maybe selected to be included as part of a keyword summary section of anextractive summarization for the multi-party communication transcriptdata object. Accordingly, each candidate keyword of a multi-partycommunication transcript data object may be associated with akeyword-related token subset of the text tokens of the multi-partycommunication transcript data object. For example, if “protected healthinformation” is a candidate keyword of the multi-party communicationtranscript data object, then the keyword-related token subset for theparticular candidate keyword may be the set of text tokens {“protected”,“health”, “information”}. In some embodiments, the candidate keywords ofa multi-party communication transcript data object are generated bycombining text tokens of the multi-party communication transcript dataobject into units based at least in part on keyword generationheuristics. For example, in some embodiments, a keyword generationheuristic may require that, each time a text token that is classified asbeing a noun by a part-of-speech tagger model follows a text token thatis classified as being an adjective by the part-of-speech tagger model,then the combination of the two text tokens is grouped into a candidatekeyword.

As another example, a keyword generation heuristic may require that eachS-sized sequence of consecutively occurring text tokens of a multi-partycommunication transcript data object is grouped into a respectivecandidate keyword, where S may be selected a range of one or more sizevalues defined by configuration data for a corresponding summarizationframework. For example, if S is selected from the size range {3, 4, 5},then the set of candidate keywords for a multi-party communicationtranscript data object includes all of the three-sized sequences ofconsecutively-occurring text tokens of the multi-party communicationtranscript data object (e.g., the sequence “significant medicalmilestone), all of the four-sized sequences of consecutively-occurringtext tokens of the multi-party communication transcript data object(e.g., the sequence “very significant medical milestone”), and all ofthe five-sized sequences of consecutively-occurring text tokens of themulti-party communication transcript data object (e.g., the sequence“early signs of acute prostate cancer” assuming stop words such as “of”are removed prior to candidate keyword generation). As this exampleillustrates, depending on the set of keyword generation heuristics used,in some embodiments a text token may belong to two or more candidatekeywords.

In some embodiments, step/operation 401 may be performed in accordancewith the process that is depicted in FIG. 6 . As depicted in FIG. 6 ,the depicted process begins when the multi-party communicationtranscript data object 601 (or other document data object) is“tokenized” to generate a set of N text tokens 602 (e.g., a set of Nwords) and to assign, to each text token, a part-of-speech tag using apart-of-speech tagger model. Then, the text tokens 602 are combined(e.g., in accordance with a set of keyword generation heuristics) togenerate a set of K candidate keywords. Afterward, the N text tokens areprocessed using a cross-token attention machine learning model 603 togenerate, for each text token, an attention-based token representation.Accordingly, the cross-token attention machine learning model 603 isconfigured to generate N attention-based token representations 604, eachbeing the attention-based token representation for a respective texttoken of the N text tokens 602.

In some embodiments, a cross-token attention machine learning model is amachine learning model that is configured to generate, for each texttoken of N text tokens of a multi-party communication transcript dataobject, an attention-based token representation. In some embodiments,the cross-token attention machine learning model is further configuredto: (i) generate a set of N*N token pairs each comprising a pair of texttokens of the N text tokens of the multi-party communication transcriptdata object, (ii) for each token pair, generate a cross-tokenself-attention weight (e.g., using a self-attention mechanism), and (ii)generate the attention-based token representation for the text tokenbased at least in part on (e.g., by applying a non-linear function to alinear combination of) each cross-token self-attention weight that isassociated with the text token.

In some embodiments, during training, the cross-token attention machinelearning model is trained using a language modeling task, such as atoken making task that comprises: (i) masking a randomly-selectedM-sized subset of the N text tokens of a training document data object(e.g., a training multi-party communication transcript data object);(ii) processing initial token representations (e.g., bag of wordsrepresentations) of the (T−M) non-masked tokens of the training documentdata object and M masked token representations (e.g., all-zero vectorrepresentations) of the masked tokens of the training document dataobject in accordance with a current state of trainable parameters of thecross-token attention machine learning model to: (a) for each token pairof N*N token pairs that comprises a pair of text tokens (including tokenpairs that comprise masked text tokens), generate a cross-tokenself-attention token, (b) for each text token that is in N token pairsand thus is associated with N cross-token self-attention weights,generate an attention-based token representation based at least in parton a linear combination of a vector of the N cross-token self-attentionweights for the text token and a vector of the N initial tokenrepresentations for all of the N text tokens, and (c) predict the Mmasked tokens of the training document data object based at least inpart on the N attention-based token representations for the N texttokens of the training document data object; and updating the trainableparameters of the cross-token attention machine learning model tominimize an error measure describing an estimated/computed error betweenthe predicted M masked tokens and the actual/ground-truth M maskedtokens. In this example, it is to be understood that while N may bedefined by the size of the training document data object, M may bedefined by a hyperparameter of the cross-token attention machinelearning model.

In some embodiments, once trained and during runtime, the cross-tokenattention machine learning model uses the resulting trained parametersto process initial token representations (e.g., bag of wordsrepresentations) of text tokens of an input document data object togenerate, for each text token, a respective attention-based tokenrepresentation. For example, in some embodiments, during runtime, thecross-token machine learning model: (i) receives N input vectors eachcomprising an initial token representation of N initial tokenrepresentations for a respective text token of N text tokens of theinput document data object, (ii) for each token pair of N token pairsthat comprises two text tokens, generates, using the trained parametersresulting from the training operations, an attention-based tokenrepresentation, and (iii) for each text token that is in N token pairsand thus is associated with N cross-token self-attention weights,generates an attention-based token representation based at least in parton a linear combination of a vector of the N cross-token self-attentionweights for the text token and a vector of the N initial tokenrepresentations for all of the N text tokens. Accordingly, the input tothe cross-token attention machine learning model may include a set of Ninput token representation vectors for the N input tokens of an inputdocument data object, while the output of the cross-token attentionmachine learning model may include a set of N attention-based tokenrepresentations for the N input tokens of an input document data object.In this example, it is to be understood that N may be defined by thesize of the training document data object.

Accordingly, the outputs of a cross-token attention machine learningmodel may include one or both of attention-based token representationsfor individual text tokens (e.g., as a final runtime output) andcross-token self-attention weights for token pairs (e.g., as anintermediate token output). In some embodiments, an attention-basedtoken representation describes a computed/predicted fixed-sizerepresentation of a corresponding text token that may be generated basedat least in part on cross-token self-attention weights that describerelative semantic correlation of the corresponding token with othertokens of a corresponding document data object. In some embodiments, across-token self-attention weight describes a computed/predicted measureof semantic relevance of a first text token of a document data objectfor a second text token of the document data object. In someembodiments, the cross-token self-attention weights for token pairs aredetermined based at least in part on the trained parameters of across-token attention machine learning model. Accordingly, thecross-token self-attention weights may be dynamically generated values.

Returning to FIG. 6 , once the N attention-based token representations604 are generated, the N attention-based token representations 604 areused to generate, for each candidate keyword of the K candidate keywordsof the multi-party communication transcript data object, a token-basedkeyword representation. Accordingly, the N attention-based tokenrepresentations 604 are used to generate K token-based keywordrepresentations 605, comprising a respective token-based keywordrepresentation for each candidate keyword of the K candidate keywords.

In some embodiments, a token-based keyword representation is afixed-size representation of a corresponding candidate keyword that isgenerated based at least in part on the attention-based keywordrepresentations that are in the candidate keyword (i.e., that are in thekeyword-related token subset for the candidate keyword). In someembodiments, when an ith candidate keyword comprises N text tokens andthus N attention-based keyword representations, the token-based keywordrepresentation is generated based at least in part on (e.g., byaveraging, by concatenating, by processing using one or more trainedneural network layers, and/or the like) the N attention-based keywordrepresentations. Accordingly, in some embodiments, a token-based keywordrepresentation provides a computed/predicted representation of acorresponding candidate keyword that reflects hidden representations ofthe text tokens in the corresponding candidate keyword as generatedusing a cross-token attention mechanism, such as the cross-tokenattention mechanism described above in relation to a cross-tokenattention machine learning model.

Returning to FIG. 6 , the predictive data analysis computing entity 106also generates a token-based document representation 606 for themulti-party communication transcript data object based at least in parton the N attention-based token representations 604. In some embodiments,a token-based document representation is a fixed-size representation ofa multi-party communication transcript data object that is generatedbased at least in part on all of the attention-based tokenrepresentations for all of the text tokens of the multi-partycommunication transcript data object. In some embodiments, given amulti-party communication transcript data object that comprises N texttokens and is thus associated with N attention-based tokenrepresentations, the token-based document representation is generatedbased at least in part on (e.g., by performing a max pooling operationon, by averaging, by concatenating, by processing using one or moretrained neural network layers, and/or the like) the N attention-basedtoken representations. Accordingly, in some embodiments, a token-baseddocument representation provides a computed/predicted representation ofa corresponding document data object that reflects hiddenrepresentations of the text tokens in the corresponding document dataobject as generated using a cross-token attention mechanism, such as thecross-token attention mechanism described above in relation to across-token attention machine learning model.

Returning to FIG. 6 , once the token-based document representation 606for the multi-party communication transcript data object and the Ktoken-based keyword representations 605 for the K candidate keywords ofthe multi-party communication transcript data object are generated, thetoken-based document representation 606 and the K token-based keywordrepresentations 605 are used to generate, for each candidate keyword ofthe K candidate keywords, a respective document-keyword similarityscore. Accordingly, K document-keyword similarity scores 607 aregenerated, comprising a respective document-keyword similarity score foreach candidate keyword of K candidate keywords of the multi-partycommunication transcript data object.

In some embodiments, a document-keyword similarity score is acomputed/predicted measure of similarity for a respective candidatekeyword with respect to the multi-party communication transcript dataobject that comprises the respective candidate keyword. In someembodiments, the document-keyword similarity score for a respectivecandidate keyword is generated based at least in part on acomputed/predicted distance measure between a token-based documentrepresentation for the multi-party communication transcript data objectin which the respective candidate keyword occurs and a token-basedkeyword representation for the respective candidate keyword. In someembodiments, the document-keyword similarity score for a respectivecandidate keyword is generated based at least in part on acomputed/predicted normalized distance measure between a token-baseddocument representation for the multi-party communication transcriptdata object in which the respective candidate keyword occurs and atoken-based keyword representation for the respective candidate keyword.In some embodiments, the document-keyword similarity score for an ithcandidate keyword and a document data object d is generated as theoutput of the equation

${{p\left( h_{i} \right)} = \frac{R\left( h_{i} \right)}{\sum\limits_{k = 1}^{K}{R\left( h_{k} \right)}}},$where: (i) each R(h_(j)) is a non-normalized document-keyword similarityscore for the jth candidate keyword of U candidate keywords of d thatmay be calculated using the output of the equation

${R\left( h_{j} \right)} = \frac{1}{{h_{d} - h_{j}}}$(where h_(d) is the token-based document representation for d and h_(j)is the token-based keyword representation for the jth candidate keywordof U candidate keywords of d), and (ii) k is an index variable thatiterates over the K candidate keywords of d.

Returning to FIG. 6 , once the cross-token attention machine learningmodel generates the N*N cross-token self-attention weights for the N*Ntoken pairs of the multi-party communication transcript data object, thepredictive data analysis computing entity 106 uses the N*N cross-tokenself-attention weights to generate a local keyword correlation graphdata object 608. In some embodiments, the local keyword correlationgraph data object describes semantic correlations between candidatekeywords of a multi-party communication transcript data object, such assemantic correlations determined based at least in part on cross-tokenself-attention weights for token pairs of the multi-party communicationtranscript data object. In some embodiments, the local keywordcorrelation graph data object for a multi-party communication transcriptdata object describes a graph having nodes/vertices that correspond tocandidate keywords of the multi-party communication transcript dataobject and edges/links associated with edge/link weights that correspondto semantic correlations between candidate keyword pairs associated withnode/vertex pairs of the graph.

For example, in some embodiments, given a multi-party communicationtranscript data object that is associated with K candidate keywords, thelocal keyword correlation graph data object may comprise: (i) K keywordnodes each associated with a respective keyword of the K candidatekeywords, and (ii) up to K*K keyword correlation edges (e.g., K*Kkeyword correlation edges if the graph is fully connected, less than K*Kkeyword correlation edges if those keyword correlation edges having anon-threshold-satisfying keyword correlation edge weight are removedfrom the graph, and/or the like), where each keyword correlation edge isassociated with a candidate keyword pair (i.e., a pair of candidatekeywords of the K candidate keywords). In some embodiments, given akeyword correlation edge that is associated with a candidate keywordpair comprising a first candidate keyword and a second candidatekeyword, the keyword correlation edge for the noted keyword correlationedge is generated by: (i) identifying all token pairs that relate to thekeyword correlation edge such that each related token pair comprises atext token that appears in the first candidate keyword and a text tokenthat appears in the second candidate keyword, and (ii) generating thekeyword correlation edge for the noted keyword correlation edge based atleast in part on (e.g., by performing a max pooling operation on, byaveraging, by concatenating, by processing using one or more trainedneural network layers, and/or the like) all of the cross-tokenself-attention weights for the related token pairs identified in (1).For example, in an exemplary embodiment, given a first candidate keywordthat comprises N₁ text tokens and a second candidate keyword thatcomprises N₂ text tokens, then the two candidate keywords are associatedwith N₁*N₂ token pairs and thus N₁*N₂ cross-token self-attentionweights, which can then be combined (e.g., averaged) to generate thekeyword correlation edge weight for the keyword correlation edge thatconnects the keyword node for the first candidate keyword and thekeyword node for the second candidate keyword.

Returning to FIG. 6 , once the K document-keyword similarity scores 607and the local keyword correlation graph data object 608 are generated,the predictive data analysis computing entity 106 generates a keywordscore for each candidate keyword based at least in part on the Kdocument-keyword similarity scores 607 and the local keyword correlationgraph data object 608. Accordingly, K keyword scores 609 are generated,comprising a respective keyword score for each candidate keyword of theK candidate keywords of the multi-party communication transcript dataobject.

In some embodiments, a keyword score is a computed/predictedsemantic/summarization-related significance measure for a respectivecandidate keyword in a respective multi-party communication transcriptdata object that is generated based at least in part on thedocument-keyword similarity score for the candidate keyword and a localkeyword correlation graph data object. In some embodiments, to generatethe keyword scores for a set of candidate keywords of a multi-partycommunication transcript data object, the set of candidate keywords areranked by applying a page rank model/algorithm to the local keywordcorrelation graph data object associated with the multi-partycommunication transcript data object, where the prior rankings of thepage rank model/algorithm are generated based at least in part on aninitial ranking determined in accordance with the document-keywordsimilarity score for the candidate keyword. In some embodiments, whilethe keyword correlation edge weights of a local keyword correlationgraph data object are generated based at least in part on cross-tokenattention weights generated using a “local” document-specific model andthus represent more “local” and document-specific measures of semanticsignificance, the document-keyword similarity scores are generated basedat least in part on distance measures generated using keywordrepresentations are thus represent more “global” andcross-document/cross-document-corpus measures of keyword semanticsignificance, and thus by using both the local keyword correlation graphdata object and the document-keyword similarity scores, a proposedsolution can use both “local” and “global” predictive inferences ingenerating keyword scores. In some embodiments, the output of the pagerank model/algorithm is a ranking of the candidate keywords, and thekeyword score for a candidate keyword is determined based at least inpart on the position of the candidate keyword within the ranking (e.g.,an inverse of the position of the candidate keyword in a descendingranking, the actual position of the candidate keyword in an ascendingranking, and/or the like).

In some embodiments, once the K keyword scores 609 for the K candidatekeywords of the multi-party communication transcript data object aregenerated, then a refined/filtered subset of the K candidate keywords isselected based at least in part on the K keyword scores. For example,the top A candidate keywords that have the top A keyword scores may beselected, the top B percentage of the candidate keywords that have thetop B percent keyword scores may be selected, or the candidate keywordswhose keyword scores satisfy (e.g., fall above) a lower-bound keywordscore threshold may be selected (where all three of A, B, and C may bedefined by configuration hyperparameter data for the predictive dataanalysis system 101). In some embodiments, once the refined/filteredsubset of the K candidate keywords is selected, the candidate keywordsthat fall outside of this refined/filtered subset are removed, such thatthe set of candidate keywords is updated to include only the candidatekeywords in the refined/filtered subset, and K is updated to reflect thesize of the refined/filtered subset. In some embodiments, when thiskeyword refinement/filtering is performed, then the integer linearprograming optimization constraints of the integer linear programmingjoint keyword-utterance optimization model do not need to include akeyword summary length constraint requiring that a selected keywordcount of the selected keyword subset satisfies an upper-bound keywordselection count threshold (e.g., that the number of selected candidatekeywords falls below a particular upper-limit threshold count), asthresholding the count of candidate keywords in the selected keywordsubset may be performed before the integer linear programming operationsof the integer linear programming joint keyword-utterance optimizationmodel. However, in some embodiments, even when the above-describedkeyword refinement/selection is performed to refine the set of Kcandidate keywords based at least in part on the keyword scores andupdate K, the integer linear programing optimization constraint is stillrequired to select a subset of this refined set of set of K candidatekeywords, and thus in some embodiments integer linear programingoptimization constraints of the integer linear programming jointkeyword-utterance optimization model include a keyword summary lengthconstraint requiring that a selected keyword count of the selectedkeyword subset satisfies an upper-bound keyword selection countthreshold.

Moreover, as described above, in some embodiments, step/operation 401may be performed without performing the linear integer programmingoperations of the integer linear programming joint keyword-utteranceoptimization model. In some of those embodiments, once the K keywordscores 609 for the K candidate keywords of the multi-party communicationtranscript data object are generated, then a refined/filtered subset ofthe K candidate keywords is selected based at least in part on the Kkeyword scores, and the refined/filtered subset is presented as aselected keyword subset that is part of an extractive summarization of atarget document data object. In some embodiments, the extractivesummarization of the target document data object comprises a ranked listof the refined/filtered subset as generated based at least in part on adescending ranking of keyword scores for the candidate keywords in therefined/filtered subset.

Returning to FIG. 4 , at step/operation 402, the predictive dataanalysis computing entity 106 generates, for each utterance of Uutterances of the multi-party communication transcript data object, anutterance score. An example of an utterance is a sentence of themulti-party communication transcript data object.

In some embodiments, an utterance is a collection of one or more texttokens (e.g., words) of a multi-party communication transcript dataobject (or other document data object) that are determined (e.g., by asentence detection model, such as a sentence detection model thatoperates on the output of a part of speech tagger model) to constitute asemantically complete unit. In some embodiments, each utterance of amulti-party communication transcript data object may be selected to beincluded as part of a general utterance summary section of an extractivesummarization for the multi-party communication transcript data objector a party-specific utterance summary section of an extractivesummarization for the multi-party communication transcript data object.In some embodiments, each utterance of a multi-party communicationtranscript data object is associated with (e.g., is recorded to havebeen uttered by) a party profile of the P party profiles of themulti-party communication transcript data object. In some embodiments,each utterance of a multi-party communication transcript data object maybe associated with an utterance-related token subset of the text tokensof the multi-party communication transcript data object that includesall of the tokens in the utterance. For example, if “can you still hearme?” is an utterance of the multi-party communication transcript dataobject, then the utterance-related token subset for the particularutterance may be the set of text tokens {“can”, “you”, “still”, “hear”,“me”}. In some embodiments, the utterance of a multi-party communicationtranscript data object is generated by combining text tokens of themulti-party communication transcript data object into units based atleast in part on utterance generation heuristics. For example, in someembodiments, an utterance generation heuristic may require that eachconsecutive sequence of text tokens of a multi-party communicationtranscript data object that appear between two dot text symbols becategorized as an utterance of the multi-party communication transcriptdata object.

In some embodiments, step/operation 402 may be performed in accordancewith the process that is depicted in FIG. 7 , which is an exampleprocess for generating U utterance scores comprising a respectiveutterance score for each utterance of U utterances of a multi-partycommunication transcript data object. The process that is depicted inFIG. 7 begins at step/operation 701 when the predictive data analysiscomputing entity 106 generates, for each utterance, an attention-basedutterance representation. In some embodiments, to generate the Uattention-based utterance representations for the U utterances of themulti-party communication transcript data object, the predictive dataanalysis computing entity 106 uses a cross-utterance attention machinelearning model.

In some embodiments, a cross-utterance attention machine learning modelis configured to generate, for each utterance of a set of utterances ofa multi-party communication transcript data object, an attention-basedutterance representation using a cross-utterance self-attentionmechanism. In some embodiments, given a set of U utterances in amulti-party communication transcript data object that are in turnassociated with U respective initial utterance representations (witheach utterance being associated with a respective initial utterancerepresentation), the cross-utterance attention machine learning model:(i) for each utterance pair of U*U utterance pairs that comprises twoutterances from the multi-party communication transcript data object,generate a cross-utterance self-attention weight based at least in parton the trained parameters of the cross-utterance attention machinelearning model, and (ii) for each utterance of the U utterances that isin U utterance pairs and thus is associated with U cross-utteranceself-attention weights, generate the attention-based utterancerepresentation for the utterance by applying a non-linear transformationto a linear combination output of a first vector comprising the Uinitial utterance representations and the U cross-utteranceself-attention weights for the U utterance pairs that comprise theparticular utterance. In some of the noted embodiments, thecross-utterance attention machine learning model is itself ahierarchical attention machine learning model, such that the U initialutterance representations are themselves generated using a cross-tokenself-attention mechanism, with each initial utterance representation foran utterance u that comprises N^(u) text tokens is generated by: (i)receiving N initial token representations (e.g., bag of wordsrepresentations, attention-based token representations generated by thecross-token attention machine learning model, and/or the like) for theN^(u) text tokens of the utterance u as well as a default tokenrepresentation, (ii) for each token pair of N^(u)*N^(u) token pairsassociated with the N^(u) tokens of the utterance u, generating across-token self-attention weight based at least in part on the trainedparameters of the cross-utterance attention machine learning model,(iii) for each token of the N^(u) tokens of the utterance u that is inN^(u) token pairs and thus is associated with N^(u) cross-tokenself-attention weights, generating an attention-based tokenrepresentation by applying a non-linear transformation to a linearcombination output of a first vector of the N^(u) initial tokenrepresentations in the utterance u and a second vector of N^(u)cross-token attention weights for token pairs that comprise theparticular token, and (iv) combining the N^(u) attention-based tokenrepresentations to generate the initial utterance representation for theutterance u. Examples of hierarchical attention machine learning modelsare described in Zhang et al., HIBERT. Document Level Pre-training ofHierarchical Bidirectional Transformers for Document Summarization(2019), arXiv:1905.06566v1 [cs.CL],https://doi.org/10.48550/arXiv.1905.06566.

In some embodiments, the initial utterance representations used by across-utterance attention machine learning model are generated usingmechanisms other than the hierarchical attention mechanisms describedabove. For example, in some embodiments, given an utterance thatincludes a set of text tokens, the initial utterance representation forthe utterance is generated based at least in part on (e.g., byaveraging, by concatenating, by processing using one or more trainedneural network layers, and/or the like) the attention-based tokenrepresentations for the set of text tokens as generated by a cross-tokenattention machine learning model. As another example, in someembodiments, given an utterance that includes a set of text tokens, theinitial utterance representation for the utterance is generated based atleast in part on (e.g., by averaging, by concatenating, by processingusing one or more trained neural network layers, and/or the like) bag ofwords representations for the set of text tokens as generated by across-token attention machine learning model. As yet another example, insome embodiments, given an utterance that includes a set of text tokens,the initial utterance representation for the utterance is generatedbased at least in part on (e.g., by averaging, by concatenating, byprocessing using one or more trained neural network layers, and/or thelike) one-hot-coded representations for the set of text tokens asgenerated by a cross-token attention machine learning model.

In both hierarchical and non-hierarchical architectures, thecross-utterance attention machine learning model may be trained using alanguage modeling task, such as an utterance masking task thatcomprises: (i) masking a randomly-selected subset of J utterances from Uutterances of a training document data object, (ii) generatingattention-based utterance representations for the U-J non-maskedutterances using a current state of trainable parameters of thecross-utterance attention machine learning model, (iii) predicting the Jmasked utterances based at least in part on the U-J attention-basedutterance hidden representations for the U-J non-masked utterances, (iv)updating trainable parameters of the cross-utterance attention machinelearning model to minimize an error measure that is determined based atleast in part on a deviation of the predicted masked utterances with thereal/ground truth masked utterances.

In some embodiments, inputs to the cross-utterance attention machinelearning model include N vectors comprising N initial tokenrepresentations for N text tokens of an input document data object. Insome embodiments, inputs to the cross-utterance attention machinelearning model include U vectors comprising U initial utterancerepresentations for U utterances of an input document data object. Insome embodiments, outputs of the cross-utterance attention machinelearning model comprise U vectors comprising U attention-basedutterances representations for U utterances of an input document dataobject.

An operational example of a cross-utterance attention machine learningmodel 800 that has a hierarchical architecture is depicted in FIG. 8 .As depicted in FIG. 8 , the cross-utterance attention machine learningmodel 800 comprises a token-level transformer 801 that is configured to:(i) generate, for each text token of an utterance, an attention-basedtoken representation, and (ii) generate, for the utterance and based atleast in part on the attention-based token representations for the texttokens of the utterance, an initial utterance representation. As furtherdepicted in FIG. 8 , the cross-utterance attention machine learningmodel 800 further comprises an utterance/sentence level transformer 802that is configured to generate, for each utterance of the input documentdata object, an attention-based utterance representation based at leastin part on initial utterance representations generated by thetoken-level transformer 801.

Returning to FIG. 7 , at step/operation 702, the predictive dataanalysis computing entity 106 generates an utterance-based documentrepresentation for the multi-party communication transcript data objectbased at least in part on the U attention-based utterancerepresentations for the U utterances of the multi-party communicationtranscript data object. In some embodiments, an utterance-based documentrepresentation is a fixed-size representation of a multi-partycommunication transcript data object that is generated based at least inpart on all of the attention-based utterance representations for all ofthe utterances of the multi-party communication transcript data object.In some embodiments, given a multi-party communication transcript dataobject that comprises U utterances and is thus associated with Uattention-based utterance representations, the utterance-based documentrepresentation is generated based at least in part on (e.g., byperforming a max pooling operation on, by averaging, by concatenating,by processing using one or more trained neural network layers, and/orthe like) the U attention-based utterance representations. Accordingly,in some embodiments, an utterance-based document representation providesa computed/predicted representation of a corresponding document dataobject that reflects hidden representations of the utterances in thecorresponding document data object as generated using a cross-utteranceattention mechanism, such as the cross-utterance attention mechanismdescribed above in relation to a cross-utterance attention machinelearning model.

At step/operation 703, the predictive data analysis computing entity 106generates, for each utterance of the U utterances of the multi-partycommunication transcript data object, a document-utterance similarityscore. N utterance of N utterances of d. In some embodiments, adocument-utterance similarity score is a computed/predicted measure ofsimilarity for a respective utterance with respect to the multi-partycommunication transcript data object that comprises the respectiveutterance. In some embodiments, the document-utterance similarity scorefor a respective utterance is generated based at least in part on acomputed/predicted distance measure between an utterance-based documentrepresentation for the multi-party communication transcript data objectin which the respective utterance occurs and an attention-basedutterance representation for the respective utterance. In someembodiments, the document-utterance similarity score for a respectiveutterance is generated based at least in part on a computed/predictednormalized distance measure between an utterance-based documentrepresentation for the multi-party communication transcript data objectin which the respective utterance occurs and an attention-basedutterance representation for the respective utterance. In someembodiments, the document-utterance similarity score for an ithutterance and a document data object d is generated as the output of theequation

${{{pu}\left( h_{i} \right)} = \frac{{Ru}\left( h_{i} \right)}{\sum\limits_{u = 1}^{U}{{Ru}\left( h_{u} \right)}}},$where: (i) each Ru(h_(j)) is a non-normalized document-utterancesimilarity score for the jth utterance of U utterances of d that may becalculated using the output of the equation

${{Ru}\left( h_{j} \right)} = \frac{1}{{h^{d} - h^{j}}}$(where h^(d) is the utterance-based document representation for d andh^(j) is the attention-based utterance representation for the jthutterance of U utterances of d), and (ii) u is an index variable thatiterates over the U utterances of d.

At step/operation 704, the predictive data analysis computing entity 106generates a local utterance correlation graph data object. In someembodiments, the local utterance correlation graph data object describessemantic correlations between utterances of a multi-party communicationtranscript data object, such as semantic correlations determined basedat least in part on cross-utterance self-attention weights for utterancepairs of the multi-party communication transcript data object. In someembodiments, the local utterance correlation graph data object for amulti-party communication transcript data object describes a graphhaving nodes/vertices that correspond to utterances of the multi-partycommunication transcript data object and edges/links associated withedge/link weights that correspond to semantic correlations betweenutterance pairs associated with node/vertex pairs of the graph. Forexample, in some embodiments, given a multi-party communicationtranscript data object that is associated with U utterances, the localutterance correlation graph data object may comprise: (i) U utterancenodes each associated with a respective utterance of the U utterances,and (ii) up to U*U utterance correlation edges (e.g., U*U utterancecorrelation edges if the graph is fully connected, less than U*Uutterance correlation edges if those utterance correlation edges havinga non-threshold-satisfying utterance correlation edge weight are removedfrom the graph, and/or the like), where each utterance correlation edgeis associated with an utterance pair (i.e., a pair of utterances of theU utterances). In some embodiments, given an utterance correlation edgethat is associated with an utterance pair comprising a first utteranceand a second utterance, the utterance correlation edge for the notedutterance correlation edge is generated based at least in part on thecross-utterance self-attention weight for the utterance pair asgenerated by the cross-utterance attention machine learning model.

As described above, in some embodiments, the outputs of across-utterance attention machine learning model may include one or bothof attention-based utterance representations for individual utterances(e.g., as a final runtime output) and cross-utterance self-attentionweights for utterance pairs (e.g., as an intermediate utterance output).In some embodiments, an attention-based utterance representationdescribes a computed/predicted fixed-size representation of acorresponding utterance that may be generated based at least in part oncross-utterance self-attention weights that describe relative semanticcorrelation of the corresponding utterance with other utterances of acorresponding document data object. In some embodiments, across-utterance self-attention weight describes a computed/predictedmeasure of semantic relevance of a first utterance of a document dataobject for a second utterance of the document data object. In someembodiments, the cross-utterance self-attention weights for utterancepairs are determined based at least in part on the trained parameters ofa cross-utterance attention machine learning model. Accordingly, thecross-utterance self-attention weights may be dynamically generatedvalues.

At step/operation 705, the predictive data analysis computing entity 106generates an utterance score for each utterance based at least in parton the U document-utterance similarity scores and the local utterancecorrelation graph data object. Accordingly, U utterance scores aregenerated, comprising a respective utterance score for each utterance ofthe U utterances of the multi-party communication transcript dataobject.

In some embodiments, an utterance score is a computed/predictedsemantic/summarization-related significance measure for a respectiveutterance in a respective multi-party communication transcript dataobject that is generated based at least in part on thedocument-utterance similarity score for the utterance and a localutterance correlation graph data object. In some embodiments, togenerate the utterance scores for a set of utterances of a multi-partycommunication transcript data object, the set of utterances are rankedby applying a page rank model/algorithm to the local utterancecorrelation graph data object associated with the multi-partycommunication transcript data object, where the prior rankings of thepage rank model/algorithm are generated based at least in part on aninitial ranking determined in accordance with the document-utterancesimilarity score for the utterance. In some embodiments, while theutterance correlation edge weights of a local utterance correlationgraph data object are generated based at least in part oncross-utterances attention weights generated using a “local”document-specific model and thus represent more “local” anddocument-specific measures of semantic significance, thedocument-utterance similarity scores are generated based at least inpart on distance measures generated using utterance representations arethus represent more “global” and cross-document/cross-document-corpusmeasures of utterance semantic significance, and thus by using both thelocal utterance correlation graph data object and the document-utterancesimilarity scores, a proposed solution can use both “local” and “global”predictive inferences in generating utterance scores. In someembodiments, the output of the page rank model/algorithm is a ranking ofthe utterances, and the utterance score for an utterance is determinedbased at least in part on the position of the utterance within theranking (e.g., an inverse of the position of the utterance in adescending ranking, the actual position of the utterance in an ascendingranking, and/or the like).

In some embodiments, once the U utterance scores for the U utterances ofthe multi-party communication transcript data object are generated, thena refined/filtered subset of the U utterances is selected based at leastin part on the U utterance scores. For example, the top A utterancesthat have the top A utterance scores may be selected, the top Bpercentage of the utterances that have the top B percent utterancescores may be selected, or the utterances whose utterance scores satisfy(e.g., fall above) a lower-bound utterance score threshold may beselected (where all three of A, B, and C may be defined by configurationhyperparameter data for the predictive data analysis system 101). Insome embodiments, once the refined/filtered subset of the U utterancesis selected, the utterances that fall outside of this refined/filteredsubset are removed, such that the set of utterances is updated toinclude only the utterances in the refined/filtered subset, and U isupdated to reflect the size of the refined/filtered subset.

Moreover, as described above, in some embodiments, step/operation 402may be performed without performing the linear integer programmingoperations of the integer linear programming joint keyword-utteranceoptimization model. In some of those embodiments, once the U utterancescores for the U utterances of the multi-party communication transcriptdata object are generated, then a refined/filtered subset of the Uutterances is selected based at least in part on the U utterance scores,and the refined/filtered subset is presented as a selected utterancesubset that is part of an extractive summarization of a target documentdata object. In some embodiments, the extractive summarization of thetarget document data object comprises a ranked list of therefined/filtered subset as generated based at least in part on adescending ranking of utterance scores for the utterances in therefined/filtered subset.

Returning to FIG. 4 , at step/operation 403, the predictive dataanalysis computing entity 106 generates the extractive summarizationbased at least in part on the K keyword scores for the K candidatekeywords of the multi-party communication transcript data object (e.g.,the original K candidate keywords, the refined/filtered K candidatekeywords, and/or the like) and the U utterance scores for the Uutterances of the multi-party communication transcript data object(e.g., the original U utterances, the refined/filtered U utterances,and/or the like). In some embodiments, the predictive data analysiscomputing entity 106 processes the K keyword scores and the U utterancescores for a multi-party communication transcript data object using aninteger linear programming joint keyword-utterance optimization modelthat is configured to generate a selected utterance subset of the Uutterances of the multi-party communication transcript data object and aselected keyword subset of the K candidate keywords of the multi-partycommunication transcript data object in a manner that is configured tomaximize a joint keyword-utterance score for the selected utterancesubset and the selected keyword subset given one or more integer linearprogramming optimization constraints. In some embodiments, the integerlinear programming optimization constraints of the integer linearprogramming joint keyword-utterance optimization model comprise at leastone of a party utterance summary length constraint requiring that eachparty utterance summary satisfies an upper-bound party utterance summarylength threshold, a keyword-based utterance coverage constraintrequiring that, if the selected keyword subset comprises a particularcandidate keyword, the selected utterance subset comprises at least oneutterance that comprises the particular candidate keyword, anutterance-based keyword coverage constraint requiring that, if theselected utterance subset comprises a particular candidate keyword, theselected keyword subset comprises the particular candidate keyword, anutterance non-emptiness constraint requiring that, for each partyprofile, the selected utterance subset comprises at least one utterancerelated to the party profile, a pairwise utterance selection constraintrequiring that, if a pairwise utterance similarity score for across-party utterance pair comprising a first utterance from a firstparty profile and a second utterance from a different party profilesatisfies a lower-bound pairwise utterance similarity threshold, theselected utterance subset comprises both the first utterance and thesecond utterance, or a keyword summary length constraint requiring thata selected keyword count of the selected keyword subset satisfies anupper-bound keyword selection count threshold.

In some embodiments, the integer linear programming jointkeyword-utterance optimization model uses integer linear programmingoperations to generate/select a selected utterance subset of the set ofall utterances in a document data object and a selected keyword subsetof the set of all candidate keywords in the document data object in amanner that optimizes (e.g., maximizes) an optimization measure given aset of optimization measures. The optimization measure may be a jointkeyword-utterance score for the combination of the selected utterancesubset and the selected keyword subset. In some embodiments, a jointkeyword-utterance score for the combination of a set of utterances and aset of keywords may be a relevance/selection score for the two sets thatis generated based at least in part on the utterances scores for the setof utterances and the keyword scores for the set of keywords. Forexample, given an ith candidate selected keyword subset comprisingK_(si) candidate keywords that are associated with K_(si) keyword scoresrespectively as well as a jth candidate selected utterance subsetcomprising U_(sj) utterances that are associated with U utterance scoresrespectively, the joint keyword-utterance score for the combination ofthe ith candidate selected keyword subset and the a jth candidateselected utterance subset may be generated based at least in part on theK_(si) keyword scores and the U_(sj) utterance scores.

In some embodiments, the joint keyword-utterance score for an ithcandidate selected keyword subset and a jth candidate selected utterancesubset is generated based at least in part on the output of the equation

${JS}_{i,j} = {{\sum\limits_{p = 1}^{2}{\sum\limits_{u = 1}^{U_{p}}{{{US}\left( U_{u}^{j} \right)} \star X_{u}^{j}}}} + {\sum\limits_{k = 1}^{K}{{{KS}(k)} \star {Y_{k}.}}}}$In this equation: (i) p is an index variable that iterates over two (or,in some embodiments, more than two) party profiles of the multi-partycommunication transcript data object, (ii) u is an index variable thatiterates over U_(p) utterances of the pth party profile, (iii) U_(u)^(h) is the uth utterance of the U_(p) utterances of the pth partyprofile, (iv) US(U_(u) ^(j)) is the utterance score (e.g., as determinedbased at least in part on utterance informativeness and/or ranking) ofU_(u) ^(j), (v) X_(u) ^(j) is a binary variable that is set to one ifU_(u) ^(j) is part of the jth candidate selected utterance subset and isset to zero otherwise (and is thus different across different utterancesets, allowing the resulting joint keyword-utterance score to change asthe utterance sets change), (vi) k is an index variables that iteratesover K candidate keywords of the multi-party communication transcriptdata object, (vi) KS(k) is the keyword score (e.g., as determined basedat least in part on the keyword ranking) for the kth candidate keywordof the K candidate keywords of the multi-party communication transcriptdata object, and (vii) Y_(k) is a binary variable that is set to one ifthe kth candidate keyword is part of the ith candidate selected keywordsubset and is set to zero otherwise (and is thus different acrossdifferent keyword sets, allowing the resulting joint keyword-utterancescore to change as the keyword sets change).

In some embodiments, to generate the selected keyword subset and theselected utterance subset for a multi-party communication transcriptdata object, the integer linear programming joint keyword-utteranceoptimization model: (i) identifies a set of candidate selected keywordsubsets and a set of candidate selected utterance subsets, (ii) for eachsubset pair comprising a respective candidate selected keyword subsetand a respective candidate selected utterance subset that satisfies thedefined integer linear programming optimization operations, generates ajoint keyword-utterance score, (iii) selects the subset pair thatsatisfies the defined integer linear programming optimization operationsand that has the highest joint keyword-utterance score, and (iv) setsthe candidate selected keyword subset in the selected subset pair as theoutput selected keyword subset and the candidate selected utterancesubset in the selected subset pair as the output selected utterancesubset. In some embodiments, to generate the selected keyword subset andthe selected utterance subset for a multi-party communication transcriptdata object, the integer linear programming joint keyword-utteranceoptimization model selects the values for the X and Y indicatorvariables/functions in the above equation for generating the jointkeyword-utterance score to maximize the joint keyword-utterance scorewhile observing a set of defined integer linear programming optimizationconstraints.

In some embodiments, the integer linear programming optimizationconstraints comprise a party utterance summary length constraintrequiring that each party utterance summary satisfies an upper-boundparty utterance summary length threshold. In other words, the partyutterance summary length constraint requires that, for each party, thenumber of utterances of the party that are included in the selectedutterance subset (and are thus in the party utterance summary for theparty) satisfy (e.g., fall below) an upper-bound party utterance summarylength threshold (which may be a party-specific value or may be a commonvalue across all parties). In general, any upper-bound thresholddescribed in this application may be satisfied by a value if the valuefalls below the upper-bound threshold or falls below or is equal to theupper-bound threshold, depending on the embodiment. In some embodiments,the party utterance summary length constraint does not allow selectionof a selected utterance subset that comprises a number of utterances ofeven one-party profile that is above an upper bound threshold. In someembodiments, the party utterance summary length constraint does notallow a selected utterance subset that comprises a number of utterancesof even one-party profile that is above or equal to an upper boundthreshold.

In some embodiments, the integer linear programming optimizationconstraints comprise a keyword-based utterance coverage constraintrequiring that, if the selected keyword subset comprises a particularcandidate keyword, the selected utterance subset comprises at least oneutterance that comprises the particular candidate keyword. In otherwords, the keyword-based utterance coverage constraint requires thateach candidate keyword in the selected keyword subset be included in atleast one utterance of the selected utterance subset. Accordingly, givena combination of a candidate selected keyword subset and a candidateselected utterance subset, if the candidate selected keyword subsetcomprises even one candidate keyword that is not part of any utterancesthat are in the candidate selected utterance subset, then thecombination cannot be selected as the selected keyword subset and theselected utterance subset according to the keyword-based utterancecoverage constraint.

In some embodiments, the integer linear programming optimizationconstraints comprise an utterance-based keyword coverage constraintrequiring that, if the selected utterance subset comprises a particularcandidate keyword, the selected keyword subset comprises the particularcandidate keyword. In other words, the utterance-based keyword coverageconstraint requires that all candidate keywords that appear inutterances of the selected utterance subset be in the selected keywordsubset. Accordingly, given a combination of a candidate selected keywordsubset and a candidate selected utterance subset, if even one candidatekeyword that appears in even one utterance of the candidate selectedutterance subset is not part of the candidate selected keyword subset,then the combination cannot be selected as the selected keyword subsetand the selected utterance subset according to the utterance-basedkeyword coverage constraint.

In some embodiments, the integer linear programming optimizationconstraints comprise a pairwise utterance selection constraint requiringthat, if a pairwise utterance similarity score for (e.g., a cosinesimilarity score of the respective attention-based utterancerepresentations of) a cross-party utterance pair comprising a firstutterance from a first party profile and a second utterance from adifferent party profile satisfies a lower-bound pairwise utterancesimilarity threshold, the selected utterance subset comprises both thefirst utterance and the second utterance. In other words, given a firstparty profile associated with a multi-party communication transcriptdata object that is associated with (e.g., is recorded to be thespeaker/utterer of) U¹ utterances of the multi-party communicationtranscript data object as well as a second, different party profileassociated with the multi-party communication transcript data objectthat is associated with (e.g., is recorded to be the speaker/utterer of)U² utterances of the multi-party communication transcript data object,and thus given U¹*U² cross-party utterance pairs each comprising one ofthe U¹ utterances of the first party profile and one of the U²utterances of the second party profile, then for each particularcross-party utterance pair that comprises a first utterance of the U¹utterances of the first party profile and a second utterance of the U²utterances of the second party profile, the following operations areperformed: (i) a pairwise utterance similarity score for the particularcross-party utterance pair is generated, for example based at least inpart on a similarity score (e.g., a cosine similarity score) between theattention-based utterance representation of the first utterance and theattention-based utterance representation of the second utterance, (ii) adetermination is made about whether the pairwise utterance similarityscore for the particular cross-party utterance pair satisfies (e.g.,falls above, falls above or is equal to, and/or the like) a lower-boundpairwise utterance similarity threshold, and (iii) if the determinationat (ii) shows that the pairwise utterance similarity score for theparticular cross-party utterance pair satisfies (e.g., falls above,falls above or is equal to, and/or the like) the lower-bound pairwiseutterance similarity threshold, then the pairwise utterance selectionconstraint requires that the first utterance and the second utterance beboth added to the selected utterance subset. In general, any lower-boundthreshold described in this application may be satisfied by a value ifthe value falls above the lower-bound threshold or falls above or isequal to the lower-bound threshold, depending on the embodiment.

In some embodiments, the pairwise lower-bound utterance similaritythreshold is generated based at least in part on a deviation measurebetween: (i) a maximal pairwise utterance similarity score for allcross-party utterance pairs, and (ii) a predefined maximal pairwiseutterance similarity score deviation threshold. In other words, given afirst party profile associated with a multi-party communicationtranscript data object that is associated with (e.g., is recorded to bethe speaker/utterer of) U¹ utterances of the multi-party communicationtranscript data object as well as a second, different party profileassociated with the multi-party communication transcript data objectthat is associated with (e.g., is recorded to be the speaker/utterer of)U² utterances of the multi-party communication transcript data object,and thus given U¹*U² cross-party utterance pairs each comprising one ofthe U¹ utterances of the first party profile and one of the U²utterances of the second party profile, to generate the pairwiselower-bound utterance similarity threshold the following operations areperformed: (i) for each cross-party utterance pair that comprises afirst utterance of the U¹ utterances of the first party profile and asecond utterance of the U² utterances of the second party profile, apairwise utterance similarity score is generated, thus resulting inU¹*U² pairwise utterance similarity scores, (ii) the highest pairwiseutterance similarity score of the U¹*U² pairwise utterance similarityscores is detected, (iii) the pairwise lower-bound utterance similaritythreshold is generated based at least in part on the highest pairwiseutterance similarity score minus a predefined value known as apredefined maximal pairwise utterance similarity score deviationthreshold, which may be defined by configuration data associated with acorresponding predictive data analysis system.

Returning to FIG. 4 , at step/operation 404, the predictive dataanalysis computing entity 106 performs one or more prediction-basedactions based at least in part on the extractive summarization. In someembodiments, performing the prediction-based actions comprisesgenerating display data for a prediction output user interface, such asthe prediction output user interface 900 of FIG. 9 that displays theextractive summarization for the multi-party communication transcriptdata object 500 of FIG. 5 .

Other examples of prediction-based actions include performingoperational load balancing for post-prediction systems by usingextractive summarizations to set the number of allowed computingentities used by the noted post-prediction systems. For example, in someembodiments, a predictive data analysis computing entity determines Ddocument classifications for D document data objects based at least inpart on the D extractive summarizations for the D document data objects.Then, the count of document data objects that are associated with anaffirmative document classification, along with a resource utilizationratio for each document data object, can be used to predict a predictednumber of computing entities needed to perform post-predictionprocessing operations (e.g., automated investigation operations) withrespect to the D document data objects. For example, in someembodiments, the number of computing entities needed to performpost-prediction processing operations (e.g., automated investigationoperations) with respect to D document data objects can be determinedbased at least in part on the output of the equation:

${R = {{ceil}\left( {\sum\limits_{k}^{k = K}{ur}_{k}} \right)}},$where R is me predicted number of computing entities needed to performpost-prediction processing operations with respect to the D documentdata object, cello) is a ceiling function that returns the closestinteger that is greater than or equal to the value provided as the inputparameter of the ceiling function, k is an index variable that iteratesover K document data objects among the D document data that areassociated with affirmative investigative classifications, and ur_(k) isthe estimated resource utilization ratio for a kth document data objectthat may be determined based at least in part on a count ofutterances/tokens/words in the kth document data object. In someembodiments, once R is generated, the predictive data analysis computingentity can use R to perform operational load balancing for a serversystem that is configured to perform post-prediction processingoperations (e.g., automated investigation operations) with respect to Ddocument data objects. This may be done by allocating computing entitiesto the post-prediction processing operations if the number of currentlyallocated computing entities is below R, and deallocating currentlyallocated computing entities if the number of currently allocatedcomputing entities is above R.

As described above, FIG. 4 depicts an exemplary process 400 forgenerating an extractive summarization for a multi-party communicationtranscript data object that is associated with two party profiles (e.g.,a caller party profile and an agent profile). However, while variousembodiments of the present invention describe techniques for generatingan extractive summarization for a multi-party communication transcriptdata object that is associated with two party profiles, a person ofordinary skill in the relevant technology will recognize that variousembodiments of the present invention can be used to generate anextractive summarization of any document data object, including amulti-party communication transcript that is associated with three ormore party profiles.

For example, in some embodiments, step/operation 401 can be used togenerate a keyword-based extractive summarization of any document dataobject that comprises a selected keyword subset of the K candidatekeywords of the particular document data object as selected based atleast in part on the keyword scores for the K candidate keywords,step/operation 402 can be used to generate an utterance-based extractivesummarization of any document data object that comprises a selectedutterance subset for the U utterances of the particular document dataobject as selected based at least in part on the keyword scores for theU utterances, or the combination of steps/operations 401-402 can be usedto generate an extractive summarization of any document data object thatcomprises both the keyword-based extractive summarization of theparticular document data object and the utterance-based extractivesummarization of the particular document data object.

As another example, in some embodiments, once keyword scores andutterances scores are generated using steps/operations 401-402respectively, the integer linear programming optimizations ofstep/operation 403 may be performed without using any integer linearprogramming optimization constraints that are defined based at least inpart on constraint conditions that are specific to multi-partycommunication transcript data objects to generate an extractivesummarization of any document data object that comprises the selectedkeyword subset of the K candidate keywords in the particular documentdata object and the selected utterance subset of the U utterances in theparticular document data object. In an exemplary embodiments, oncekeyword scores and utterances scores are generated usingsteps/operations 401-402 respectively, the integer linear programmingjoint keyword-utterance optimization model may generate the selectedkeyword subset and the selected utterance subset based at least in parton the keyword scores and utterances scores and using a set of integerlinear programming optimization operations that comprise at least one ofthe keyword-based utterance coverage constraint, the utterance-basedkeyword coverage constraint, the keyword summary length constraint, or autterance summary length constraint that requires that a count ofutterances in the selected utterance subset (i.e., a selected utterancecount of the selected utterance subset) satisfies (e.g., falls below,falls below or is equal to, and/or the like) an upper-bound utterancesummary length threshold.

As yet another example, in some embodiments, once keyword scores andutterances scores are generated using steps/operations 401-402respectively, the integer linear programming optimizations ofstep/operation 403 may be performed without using any integer linearprogramming optimization constraints that are defined based at least inpart on constraint conditions that are specific to two-partycommunication transcript data objects to generate an extractivesummarization of any multi-party communication transcript data objectthat comprises the selected keyword subset of the K candidate keywordsin the particular multi-party communication transcript data object andthe selected utterance subset of the U utterances in the particularmulti-party communication transcript data object. In an exemplaryembodiments, once keyword scores and utterances scores are generatedusing steps/operations 401-402 respectively, the integer linearprogramming joint keyword-utterance optimization model may generate theselected keyword subset and the selected utterance subset based at leastin part on the keyword scores and utterances scores and using a set ofinteger linear programming optimization operations that comprise atleast one of the upper-bound party utterance summary length threshold,the keyword-based utterance coverage constraint, the utterance-basedkeyword coverage constraint, the utterance non-emptiness constraint, thekeyword summary length constraint, or an P-tuple-wise utteranceselection constraint requiring that, if a tuple-wise utterancesimilarity score for a cross-party utterance P-tuple comprising anutterance from each party of P party profiles of the multi-partycommunication transcript data object satisfies (e.g., is above, is aboveor equal to, and/or the like) a lower-bound tuple-wise utterancesimilarity threshold, the selected utterance subset comprise all of theP utterances in the cross-party utterance P-tuple. In some of the notedembodiments, the tuple-wise lower-bound utterance similarity thresholdis generated based at least in part on a deviation measure between: (i)a maximal tuple-wise utterance similarity score for all cross-partyutterance P-tuples, and (ii) a predefined maximal tuple-wise utterancesimilarity score deviation threshold.

Accordingly, as described above, various embodiments of the presentinvention disclose techniques for improving storage efficiency ofdocument storage systems. As described herein, various embodiments ofthe present invention disclose techniques for generating extractivesummarizations of document data objects that comprise a selectedutterance subset of the utterances of each document data object and aselected keyword subset of the candidate keywords of each document dataobject. Because an extractive summarization of a document data object issmaller in size than the underlying document data object (as theextractive summarization includes subsets of candidate keywords andutterances described by the document data object), various embodimentsof the present invention enable storing extractive summarizations ofdocument data objects instead of the document data objects that arebigger in size. In this way, various embodiments of the presentinvention reduce storage requirements associated with storing documentdata, and thus increase storage efficiency of storing document dataassociated with document data objects. Accordingly, by generatingextractive summarizations of document data objects that comprise aselected utterance subset of the utterances of each document data objectand a selected keyword subset of the candidate keywords of each documentdata object, various embodiments of the present invention disclosetechniques for improving storage efficiency of various document storagesystems.

Moreover, as further described above, various embodiments of the presentinvention make important technical contributions to improving predictiveaccuracy of natural language processing machine learning models that areconfigured to perform natural language processing operations on documentdata objects by using an integer linear programming jointkeyword-utterance optimization model that generates a selected utterancesubset of the utterances of a document data object and a selectedkeyword subset of the candidate keywords of the document data object ina manner that is configured to maximize a joint keyword-utterance scorefor the selected utterance subset and the selected keyword subset givenone or more integer linear programming optimization constraints, anapproach which in turn improves training speed and training efficiencyof training the noted natural language processing machine learningmodels. It is well-understood in the relevant art that there istypically a tradeoff between predictive accuracy and training speed,such that it is trivial to improve training speed by reducing predictiveaccuracy, and thus the real challenge is to improve training speedwithout sacrificing predictive accuracy through innovative modelarchitectures, see, e.g., Sun et al., Feature-Frequency—Adaptive On-lineTraining for Fast and Accurate Natural Language Processing in 40(3)Computational Linguistic 563 at Abst. (“Typically, we need to make atradeoff between speed and accuracy. It is trivial to improve thetraining speed via sacrificing accuracy or to improve the accuracy viasacrificing speed. Nevertheless, it is nontrivial to improve thetraining speed and the accuracy at the same time”). Accordingly,techniques that improve predictive accuracy without harming trainingspeed, such as the techniques described herein, enable improvingtraining speed given a constant predictive accuracy. In doing so, thetechniques described herein improving efficiency and speed of trainingnatural language processing machine learning models, thus reducing thenumber of computational operations needed and/or the amount of trainingdata entries needed to train natural language processing machinelearning models. Accordingly, the techniques described herein improve atleast one of the computational efficiency, storage-wise efficiency, andspeed of training natural language processing machine learning models.

VI. CONCLUSION

Many modifications and other embodiments will come to mind to oneskilled in the art to which this disclosure pertains having the benefitof the teachings presented in the foregoing descriptions and theassociated drawings. Therefore, it is to be understood that thedisclosure is not to be limited to the specific embodiments disclosedand that modifications and other embodiments are intended to be includedwithin the scope of the appended claims. Although specific terms areemployed herein, they are used in a generic and descriptive sense onlyand not for purposes of limitation.

The invention claimed is:
 1. A computer-implemented method forgenerating an extractive summarization for a document data object, thecomputer-implemented comprising: identifying, by one or more processors,a plurality of utterances associated with the document data object; foreach utterance, by the one or more processors: generating, using across-utterance attention machine learning model, an attention-basedutterance representation, wherein the cross-utterance attention machinelearning model is configured to: (i) for each utterance pair, generate across-utterance self-attention weight, and (ii) generate theattention-based utterance representation for the utterance based atleast in part on each cross-utterance self-attention weight that isassociated with the utterance, generating, based at least in part on theattention-based utterance representation and an utterance-based documentrepresentation that is generated based at least in part on eachattention-based utterance representation, a document-utterancesimilarity score for the utterance, and generating, based at least inpart on a local utterance correlation graph data object and thedocument-utterance similarity score for the utterance, an utterancescore for the utterance, wherein each utterance correlation edge of thelocal utterance correlation graph data object corresponds to arespective utterance pair and is associated with an utterancecorrelation edge weight that is generated based at least in part on thecross-utterance self-attention weight for the respective utterance pair;generating, by the one or more processors, the extractive summarizationbased at least in part on each utterance score; and performing, by theone or more processors, one or more prediction-based actions based atleast in part on each utterance score.
 2. The computer-implementedmethod of claim 1, wherein generating the extractive summarization basedat least in part on each utterance score comprises: identifying aplurality of candidate keywords associated with the document dataobject, wherein each candidate keyword is associated with akeyword-related token subset of a group of text tokens in the documentdata object; for each text token of the group of text tokens,generating, using a cross-token attention machine learning model, anattention-based token representation, wherein the cross-token attentionmachine learning model is further configured to generate: (i) for eachtoken pair, a cross-token self-attention weight, and (ii) generate theattention-based token representation for the text token based at leastin part on each cross-token self-attention weight that is associatedwith the text token; for each candidate keyword: generating, based atleast in part on each attention-based token representation for thekeyword-related token subset for the candidate keyword, a token-basedkeyword representation for the candidate keyword, generating, based atleast in part on the token-based keyword representation and atoken-based document representation that is generated based at least inpart on each attention-based token representation, a document-keywordsimilarity score for the candidate keyword, and generating, based atleast in part on a local keyword correlation graph data object and thedocument-keyword similarity score for the candidate keyword, a keywordscore for the candidate keyword, wherein each keyword correlation edgeof the local keyword correlation graph data object corresponds to arespective candidate keyword pair and is associated with a keywordcorrelation edge weight that is generated based at least in part on eachcross-token self-attention weight for the respective candidate keywordpair; and generating the extractive summarization based at least in parton each utterance score and each keyword score.
 3. Thecomputer-implemented method of claim 2, wherein: the document dataobject is a multi-party communication transcript data object that isassociated with a plurality of party profiles, and generating theextractive summarization based at least in part on each utterance scoreand each keyword score further comprises: generating, using an integerlinear programming joint keyword-utterance optimization model and basedat least in part on each utterance score and each keyword score, aselected utterance subset of the plurality of utterances and a selectedkeyword subset of the plurality of candidate keywords, wherein: (i) theselected utterance subset comprises a party utterance summary for eachparty profile of the plurality of party profiles, (ii) the integerlinear programming joint keyword-utterance optimization model isconfigured to generate the selected utterance subset and the selectedkeyword subset to maximize a joint keyword-utterance score for theselected utterance subset and the selected keyword subset given one ormore integer linear programming optimization constraints, and (iii) theone or more integer linear programming optimization constraints comprisea party utterance summary length constraint requiring that each partyutterance summary satisfies an upper-bound party utterance summarylength threshold, and generating the extractive summarization based atleast in part on the selected utterance subset and the selected keywordsubset.
 4. The computer-implemented method of claim 3, wherein the oneor more integer linear programming optimization constraints comprise akeyword-based utterance coverage constraint requiring that, if theselected keyword subset comprises a particular candidate keyword, theselected utterance subset comprises at least one utterance thatcomprises the particular candidate keyword.
 5. The computer-implementedmethod of claim 3, wherein the one or more integer linear programmingoptimization constraints comprise an utterance-based keyword coverageconstraint requiring that, if the selected utterance subset comprises aparticular candidate keyword, the selected keyword subset comprises theparticular candidate keyword.
 6. The computer-implemented method ofclaim 3, wherein the one or more integer linear programming optimizationconstraints comprise an utterance non-emptiness constraint requiringthat, for each party profile of the plurality of party profiles, theselected utterance subset comprises at least one utterance related tothe party profile.
 7. The computer-implemented method of claim 3,wherein the one or more integer linear programming optimizationconstraints comprise a pairwise utterance selection constraint requiringthat, if a pairwise utterance similarity score for a cross-partyutterance pair comprising a first utterance from a first party profileand a second utterance from a different party profile satisfies alower-bound pairwise utterance similarity threshold, the selectedutterance subset comprises both the first utterance and the secondutterance.
 8. The computer-implemented method of claim 7, wherein thelower-bound pairwise utterance similarity threshold is generated basedat least in part on a deviation measure between: (i) a maximal pairwiseutterance similarity score for all cross-party utterance pairs, and (ii)a predefined maximal pairwise utterance similarity score deviationthreshold.
 9. The computer-implemented method of claim 3, wherein theone or more integer linear programming optimization constraints comprisea keyword summary length constraint requiring that a selected keywordcount of the selected keyword subset satisfies an upper-bound keywordselection count threshold.
 10. An apparatus for generating an extractivesummarization for a document data object, the apparatus comprising oneor more processors and at least one memory including program code, theat least one memory and the program code configured to, with the one ormore processors, cause the apparatus to: identify a plurality ofutterances associated with the document data object; for each utterance:generate, using a cross-utterance attention machine learning model, anattention-based utterance representation, wherein the cross-utteranceattention machine learning model is configured to: (i) for eachutterance pair, generate a cross-utterance self-attention weight, and(ii) generate the attention-based utterance representation for theutterance based at least in part on each cross-utterance self-attentionweight that is associated with the utterance, generate, based at leastin part on the attention-based utterance representation and anutterance-based document representation that is generated based at leastin part on each attention-based utterance representation, adocument-utterance similarity score for the utterance, and generate,based at least in part on a local utterance correlation graph dataobject and the document-utterance similarity score for the utterance, anutterance score for the utterance, wherein each utterance correlationedge of the local utterance correlation graph data object corresponds toa respective utterance pair and is associated with an utterancecorrelation edge weight that is generated based at least in part on thecross-utterance self-attention weight for the respective utterance pair;generate the extractive summarization based at least in part on eachutterance score; and perform one or more prediction-based actions basedat least in part on each utterance score.
 11. The apparatus of claim 10,wherein generating the extractive summarization based at least in parton each utterance score comprises: identifying a plurality of candidatekeywords associated with the document data object, wherein eachcandidate keyword is associated with a keyword-related token subset of agroup of text tokens in the document data object; for each text token ofthe group of text tokens, generating, using a cross-token attentionmachine learning model, an attention-based token representation, whereinthe cross-token attention machine learning model is further configuredto generate: (i) for each token pair, a cross-token self-attentionweight, and (ii) generate the attention-based token representation forthe text token based at least in part on each cross-token self-attentionweight that is associated with the text token; for each candidatekeyword: generating, based at least in part on each attention-basedtoken representation for the keyword-related token subset for thecandidate keyword, a token-based keyword representation for thecandidate keyword, generating, based at least in part on the token-basedkeyword representation and a token-based document representation that isgenerated based at least in part on each attention-based tokenrepresentation, a document-keyword similarity score for the candidatekeyword, and generating, based at least in part on a local keywordcorrelation graph data object and the document-keyword similarity scorefor the candidate keyword, a keyword score for the candidate keyword,wherein each keyword correlation edge of the local keyword correlationgraph data object corresponds to a respective candidate keyword pair andis associated with a keyword correlation edge weight that is generatedbased at least in part on each cross-token self-attention weight for therespective candidate keyword pair; and generating the extractivesummarization based at least in part on each utterance score and eachkeyword score.
 12. The apparatus of claim 11, wherein: the document dataobject is a multi-party communication transcript data object that isassociated with a plurality of party profiles, and generating theextractive summarization based at least in part on each utterance scoreand each keyword score further comprises: generating, using an integerlinear programming joint keyword-utterance optimization model and basedat least in part on each utterance score and each keyword score, aselected utterance subset of the plurality of utterances and a selectedkeyword subset of the plurality of candidate keywords, wherein: (i) theselected utterance subset comprises a party utterance summary for eachparty profile of the plurality of party profiles, (ii) the integerlinear programming joint keyword-utterance optimization model isconfigured to generate the selected utterance subset and the selectedkeyword subset to maximize a joint keyword-utterance score for theselected utterance subset and the selected keyword subset given one ormore integer linear programming optimization constraints, and (iii) theone or more integer linear programming optimization constraints comprisea party utterance summary length constraint requiring that each partyutterance summary satisfies an upper-bound party utterance summarylength threshold, and generating the extractive summarization based atleast in part on the selected utterance subset and the selected keywordsubset.
 13. The apparatus of claim 12, wherein the one or more integerlinear programming optimization constraints comprise a keyword-basedutterance coverage constraint requiring that, if the selected keywordsubset comprises a particular candidate keyword, the selected utterancesubset comprises at least one utterance that comprises the particularcandidate keyword.
 14. The apparatus of claim 12, wherein the one ormore integer linear programming optimization constraints comprise anutterance-based keyword coverage constraint requiring that, if theselected utterance subset comprises a particular candidate keyword, theselected keyword subset comprises the particular candidate keyword. 15.The apparatus of claim 12, wherein the one or more integer linearprogramming optimization constraints comprise an utterance non-emptinessconstraint requiring that, for each party profile of the plurality ofparty profiles, the selected utterance subset comprises at least oneutterance related to the party profile.
 16. The apparatus of claim 12,wherein the one or more integer linear programming optimizationconstraints comprise a pairwise utterance selection constraint requiringthat, if a pairwise utterance similarity score for a cross-partyutterance pair comprising a first utterance from a first party profileand a second utterance from a different party profile satisfies alower-bound pairwise utterance similarity threshold, the selectedutterance subset comprises both the first utterance and the secondutterance.
 17. The apparatus of claim 16, wherein the lower-boundpairwise utterance similarity threshold is generated based at least inpart on a deviation measure between: (i) a maximal pairwise utterancesimilarity score for all cross-party utterance pairs, and (ii) apredefined maximal pairwise utterance similarity score deviationthreshold.
 18. A computer program product for generating an extractivesummarization for a document data object, the computer program productcomprising at least one non-transitory computer-readable storage mediumhaving computer-readable program code portions stored therein, thecomputer-readable program code portions configured to: identify aplurality of utterances associated with the document data object; foreach utterance: generate, using a cross-utterance attention machinelearning model, an attention-based utterance representation, wherein thecross-utterance attention machine learning model is configured to: (i)for each utterance pair, generate a cross-utterance self-attentionweight, and (ii) generate the attention-based utterance representationfor the utterance based at least in part on each cross-utteranceself-attention weight that is associated with the utterance, generate,based at least in part on the attention-based utterance representationand an utterance-based document representation that is generated basedat least in part on each attention-based utterance representation, adocument-utterance similarity score for the utterance, and generate,based at least in part on a local utterance correlation graph dataobject and the document-utterance similarity score for the utterance, anutterance score for the utterance, wherein each utterance correlationedge of the local utterance correlation graph data object corresponds toa respective utterance pair and is associated with an utterancecorrelation edge weight that is generated based at least in part on thecross-utterance self-attention weight for the respective utterance pair;generate the extractive summarization based at least in part on eachutterance score; and perform one or more prediction-based actions basedat least in part on each utterance score.
 19. The computer programproduct of claim 18, wherein generating the extractive summarizationbased at least in part on each utterance score comprises: identifying aplurality of candidate keywords associated with the document dataobject, wherein each candidate keyword is associated with akeyword-related token subset of a group of text tokens in the documentdata object; for each text token of the group of text tokens,generating, using a cross-token attention machine learning model, anattention-based token representation, wherein the cross-token attentionmachine learning model is further configured to generate: (i) for eachtoken pair, a cross-token self-attention weight, and (ii) generate theattention-based token representation for the text token based at leastin part on each cross-token self-attention weight that is associatedwith the text token; for each candidate keyword: generating, based atleast in part on each attention-based token representation for thekeyword-related token subset for the candidate keyword, a token-basedkeyword representation for the candidate keyword, generating, based atleast in part on the token-based keyword representation and atoken-based document representation that is generated based at least inpart on each attention-based token representation, a document-keywordsimilarity score for the candidate keyword, and generating, based atleast in part on a local keyword correlation graph data object and thedocument-keyword similarity score for the candidate keyword, a keywordscore for the candidate keyword, wherein each keyword correlation edgeof the local keyword correlation graph data object corresponds to arespective candidate keyword pair and is associated with a keywordcorrelation edge weight that is generated based at least in part on eachcross-token self-attention weight for the respective candidate keywordpair; and generating the extractive summarization based at least in parton each utterance score and each keyword score.
 20. The computer programproduct of claim 19, wherein: the document data object is a multi-partycommunication transcript data object that is associated with a pluralityof party profiles, and generating the extractive summarization based atleast in part on each utterance score and each keyword score furthercomprises: generating, using an integer linear programming jointkeyword-utterance optimization model and based at least in part on eachutterance score and each keyword score, a selected utterance subset ofthe plurality of utterances and a selected keyword subset of theplurality of candidate keywords, wherein: (i) the selected utterancesubset comprises a party utterance summary for each party profile of theplurality of party profiles, (ii) the integer linear programming jointkeyword-utterance optimization model is configured to generate theselected utterance subset and the selected keyword subset to maximize ajoint keyword-utterance score for the selected utterance subset and theselected keyword subset given one or more integer linear programmingoptimization constraints, and (iii) the one or more integer linearprogramming optimization constraints comprise a party utterance summarylength constraint requiring that each party utterance summary satisfiesan upper-bound party utterance summary length threshold, and generatingthe extractive summarization based at least in part on the selectedutterance subset and the selected keyword subset.